Lake Victoria, the world's second-largest freshwater lake, continues to face pressure from anthropogenic activities in its catchment area, releasing pollutants, which are ultimately trapped in the sediment core, thereby posing threats to aquatic organisms. This study quantified thirteen organochlorine pesticides (OCPs) and ten polychlorinated biphenyls (PCBs) in sediments from the Uganda, Kenya, and Tanzania sides of Lake Victoria using soxhlet method, fractionation column and GC-MS/MS analysis. Total (& sum;13) OCPs levels were up to 412, 148, and 522 mu g kg-1 dry weight (d.w), and 12.1, 8.69 and 9.87 mu g kg-1 dw for total (& sum;10) PCBs for sediments from Uganda, Kenya, and Tanzania, respectively. Diagnostic ratios suggested past and ongoing use of OCPs while principal component analysis confirmed that OCP profiles were mainly due to their application in controlling pests in agriculture and public health programs, and PCB congeners were largely due to volatilization, degradation of higher PCBs into lighter PCBs, improper waste disposal of old transformers, hydraulic fluids, plasticizers, and capacitors. Ecological risk assessment highlighted that the PCB levels in sediments were below threshold effect and probable effect levels, but p,p '-DDD, and lindane were likely to pose adverse effects to sediment-dwelling organisms in Lake Victoria. These results imply persistent pollutant loads in Lake Victoria, and consequently, a need for its enhanced management.

Indoor environments are critical exposure pathways to flame retardants, yet data from Africa remain scarce. This study provides the first quantitative assessment of organophosphate esters (OPEs) and novel brominated flame retardants (NBFRs) in indoor dust from Ugandan households, establishing baseline data for East Africa. Dust samples collected from homes in Kampala were analyzed using gas chromatography-mass spectrometry (GC-MS). Concentrations of total OPEs (Sigma 5OPEs) and total NBFRs (Sigma 4NBFRs) ranged from 1520 to 102,000 ng g- 1 and 214 to 6600 ng g- 1, respectively. Tris(1,3-dichloro-2propyl) phosphate (TDCIPP) and 2,4,6-tribromophenyl allyl ether (TBP-AE) dominated OPE and NBFR profiles, respectively. Chlorinated OPEs contributed most to Sigma OPEs, reflecting emissions from building materials and consumer products. Principal component analysis indicated household items, electrical equipment, and floor finishes as the major emission sources. Estimated human exposure doses showed that ingestion was the dominant route, with children more exposed than adults. Hazard index (HI) values were below 1 for all compounds, indicating negligible non-carcinogenic risk. Carcinogenic risk (CR) values ranged from 10- 6 to 10- 12, suggesting minimal lifetime cancer risks, though children exhibited relatively higher vulnerability. Compared with global datasets, OPE and NBFR levels in Kampala were within lower-to-median range but represent the inaugural dataset for sub-Saharan Africa. These findings provide a crucial regional reference for indoor flame-retardant exposure and highlight the need for extended studies across multiple indoor microenvironments to evaluate the longterm exposure pathways and inform safer chemical management. (c) 2026 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

The enantiomer composition of chiral compounds present in food or essential oils can bear important information about their botanical origin, the technological procedure used for their production, geographical origin. Here, we describe the protocol of how to determine enantiomer ratios of important terpenes detected in wine using a two-dimensional gas chromatograph with a heart-cut switching system and solid-phase microextraction (SPME) as the sample pretreatment procedure.

Honey's botanical origin heavily influences its composition and market value, driving the need for reliable authentication methods. Monofloral honeys, derived from a single plant species, have distinct flavor and nutraceutical properties, increasing demand and making them susceptible to counterfeiting. Pollen analysis is the standard for botanical origin authentication but is labor-intensive and requires extensive regional pollen data. This study introduces a chemometric approach to discriminate honey types using volatile profiles obtained through HS-SPME-GC-MS. A dataset of 98 samples, including 49 acacia honeys, was analyzed. Second order GC-MS data were deconvoluted into temporal, spectral, and concentration profiles via Parallel Factor Analysis (PARAFAC). The resulting concentration profiles were used to develop a SIMCA (Soft Independent Modelling of Class Analogy) model, using acacia honey as the target class. The proposed strategy allowed the discrimination of acacia honey from samples of diverse botanical origin, achieving high sensitivity (86%) and specificity (92%), with an overall accuracy of 91%. Further analysis of the data revealed that acacia honeys exhibited higher levels of linalool oxide, furfural, benzaldehyde, and hotrienol, and lower levels of lilac aldehydes (B, C, D), and 1-p-menthen-9-al, compared to other types. The developed method permits honey authentication and compositional analysis, contributing to understanding honey's chemical diversity and ensuring traceability.

In recent years, the application of flow-modulated comprehensive two-dimensional gas chromatography (FM-GC × GC) has significantly increased, particularly for profiling complex food volatilomes. However, the full potential of this technique is often hindered by the complexity of instrumental optimisation, which is critical for achieving high-resolution separation across a wide variety of analytes in diverse samples. This work addresses this challenge by developing a predictive strategy to optimise FM-GC × GC conditions for the separation of a wide variety of volatile compounds. The efficiency of a local model based on peak parameters was tested across four different column setups, monitoring its correlation to the separation of 22 critical coeluting pairs. Using a Doehlert experimental design and a General Linear Model (GLM) approach, robust predictive models (R² > 0.80) were established and validated. Although the predictive models are confined to the tested experimental space, the underlying strategy and observed general behaviours provide a transferable framework for other setups. The use of a multiresponse optimisation strategy coupled to a global resolution metric, the Fraction of Resolved Peaks (FRP), successfully improved the separation of coelutions, particularly for specific column configurations. The effectiveness of this workflow was ultimately demonstrated by its successful application to the complex volatilomes of wine, honey, cascara coffee tea, and masala tea.

Archive Tokaj wines from the vineyards in the Slovak part of the Tokaj region have been characterized in terms of the content of phenolic compounds specific to that type of botrytized wine. More than 60 archive samples (1959-2017) were evaluated in terms of phenolic profile. Eighteen individual phenolic compounds were quantified by UHPLC-DAD method. The total concentration of phenolic compounds in the wines ranged from 58.28 to 302.57 mg/L. The most abundant compound in the phenolic profile was caftaric acid with average values of around 60 mg/L followed by catechin ranging from 2.24 to 49.35 mg/L, gallic acid with a mean concentration of 14.65 mg/L, and vanillic acid. Statistical evaluation using PCA and SIMCA model showed significant correlations between the phenolic profile, botrytized/non-botrytized wines, and their origin. Total antioxidant capacity determination using a CoulArray detector proved the correlation between the "putňa" number of Tokaj selections and antioxidant activity of wines.

This study explores the potential of combining ATR-FTIR and one-class classifiers for authenticating a & ccedil;ai pulp with respect the detection of the presence of undeclared materials (cassava and wheat flour). Rigorous one class Partial Least Squares Classification (PLSC) and Soft Independent Modeling of Class Analogy (SIMCA) were employed to construct classification models and in both cases the validation of number of factors was performed using an extra set of samples by procrustes cross-validation approach for raw and derived data. Both classifiers demonstrated 100% accuracy in detecting adulteration in the test set, with SIMCA excelling in sensitivity and specificity under all tested conditions. The raw spectral data provided superior results, whereas derived spectra showed reduced specificity in PLSC. The findings highlight ATR-FTIR as a robust, rapid, and eco-friendly technique for ensuring the quality and authenticity of a & ccedil;ai, addressing economic and health concerns related to food adulteration. This method presents a sustainable alternative for food quality control with minimal environmental impact.

Over the past two decades, rapid urbanization and industrialization in Uganda have generated wastewater containing emerging contaminants including per- and poly-fluoroalkyl substances (PFASs). This study assessed PFASs contamination of wastewater from Bugolobi (Kampala) and Kirinya (Jinja) wastewater treatment plants (WWTPs) by analyzing 80 influent and effluent samples for 15 PFASs using LC-MS/MS. We quantified 10 PFASs, with levels ranging from non-detectable (n.d) up to 372.4 ng/L (mean: 20.94 +/- 0.42 ng/L). At Bugolobi WWTP, influent levels ranged from n.d to 190.01 ng/L (60.85 +/- 1.03 ng/L) while effluents varied from n.d to 372.4 ng/L (237.91 +/- 7.06 ng/L). At Kirinya WWTP, influent levels ranged from n.d to 29.37 ng/L (17.58 +/- 3.54 ng/L) and effluents up to 30.21 ng/L (7.79 +/- 0.85 ng/L). Short-chain PFASs (PFBS, PFBA) were more predominant, suggesting their possible use or degradation of the long-chain PFASs. Total mass loadings were higher at Bugolobi WWTP (5353.56 mg/day), serving the more densely populated Kampala, than at Kirinya WWTP (93.62 mg/day). PFSAs exhibited higher removal (72.45 % Bugolobi; 36.45 % Kirinya) than PFCAs (-127.38 % Bugolobi; -20.50 % Kirinya), which could be attributed to their stronger hydrophobic adsorption and partial biodegradation. Bugolobi, with similar to 82.59 % total removal outperformed Kirinya (similar to 25.19 %) due to its advanced conventional treatment. Ecological risk assessment revealed higher risks at lower trophic levels at Bugolobi compared to Kirinya, likely due to lower influx and partial mitigation by its pond-based system. These findings highlight the role of WWTPs as critical point sources of PFASs, posing ecological risks to aquatic ecosystems. (c) 2025 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY-NC-ND license

Digital images have become a powerful tool for developing analytical methods in food quality control. Unlike conventional analytical signals, images can be processed to extract relevant chemical information, with chemometric techniques enhancing their utility. This review synthesizes applications of digital imaging in food analysis, providing a roadmap from univariate methods to multivariate classification/calibration approaches, illustrated through three case studies demonstrating their potential for food safety and quality. However, the field faces critical challenges, particularly the lack of methodological standardization, as evidenced by diverse applications in literature. Addressing this gap is essential to ensure reliability and reproducibility. Furthermore, the review highlights recent advances, such as hybrid color descriptors, chromaticity maps, deep learning architectures, and time-resolved RGB imaging, that improve the robustness and applicability of these techniques in food science.

Wine intolerance primarily concerns the body's ability to process histamine, sulphites, tannins, and alcohol. Histamine, a biogenic amine, is involved in immune responses and helps regulate stomach acid. The legal limits for histamine in wine range from 2 to 10 mg/mL. Accurate estimation of histamine requires a sensitive and reliable sample preparation technique. In our study, we applied stir bar sorptive extraction (SBSE) with in situ derivatization using isobutyl chloroformate, followed by gas chromatographic analysis. Under optimal experimental conditions, established through multivariate design of experiments, a limit of detection (LOD) ranging from 5 to 17 μg/L was achieved. The developed SBSE method was used to compare the biogenic amine content in botrytized wines from different regions, including Slovakia, Hungary, France, and Austria. Principal component analysis revealed partial segregation of the samples from France and Hungary.

Advanced chemical profiling of complex samples, such as botrytized wines, requires advanced analytical techniques capable of capturing subtle compositional variations. In this study, we introduce a statistically robust framework that leverages a topological data analysis (TDA) tool, Ball Mapper, in the context of comprehensive two-dimensional gas chromatography (GC × GC) with high-resolution time-of-flight mass spectrometry (HR-TOF-MS) to obtain untargeted identification of sample-specific chemical markers. A key design element of the proposed approach is its ability to numerically process the immense data volume generated per sample, whose statistical and chemical significance is often difficult to interpret using conventional methods. Each of the 34 wine samples yielded over 470,000 mass spectral functions, which were discretized, normalized, and clustered to obtain representative and relatively unique discrete mass spectral vectors in high-dimensional space. With only two interpretative parameters, the proposed framework uncovered 2,792 extracted mass spectral distributions, from which 1191 discriminative features were identified, including 334 compounds assigned to known volatile organic compound classes. The resulting chemical signatures reflected regional differences in fermentation style, grape variety, botrytization conditions, and microbial activity. Moreover, statistically robust framework of using Ball Mapper revealed consistent grouping patterns both within and between wines. These findings demonstrate that the proposed framework can support chemical characterization complex natural matrices and serve as a general strategy for analyzing any domains where GC × GC with HR-TOF-MS data are collected.

This study investigates the metabolome of high-quality hazelnuts (Corylus avellana L.) by applying untargeted and targeted metabolome profiling techniques to predict industrial quality. Utilizing comprehensive two-dimensional gas chromatography and liquid chromatography coupled with high-resolution mass spectrometry, the research characterizes the non-volatile (primary and specialized metabolites) and volatile metabolomes. Data fusion techniques, including low-level (LLDF) and mid-level (MLDF), are applied to enhance classification performance. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) reveal that geographical origin and postharvest practices significantly impact the specialized metabolome, while storage conditions and duration influence the volatilome. The study demonstrates that MLDF approaches, particularly supervised MLDF, outperform single-fraction analyses in predictive accuracy. Key findings include the identification of metabolites patterns causally correlated to hazelnut’s quality attributes, of them aldehydes, alcohols, terpenes, and phenolic compounds as most informative. The integration of multiple analytical platforms and data fusion methods shows promise in refining quality assessments and optimizing storage and processing conditions for the food industry.

The authentication of Slovak wines in comparison to other similar wines from various geographical regions, namely Hungary, France, Austria, and Ukraine, was conducted using the OC-PLS, DD-SIMCA, and PLS-DM models, all of them operating in rigorous way. The study involved 63 samples, of which 41 originated from Slovakia, covering diverse wine types such as varietal wines, cuvée selections (different “putňový”), and essence. To capture digital images under controlled conditions, a custom-made cardboard box with white inner surfaces was devised and equipped with a smartphone. During the training phase, sensitivities of 96%, 100%, and 96% were attained for OC-PLS, DD-SIMCA, and PLS-DM, respectively. In the subsequent stages of validation and testing for DD-SIMCA and PLS-DM, the proposed methods displayed optimal efficiency, achieving both sensitivity and specificity rates of 100%. However, such results were not achieved in the case of OC-PLS, which exhibited efficiency levels of 90% in validation and 80% in testing.

Background Wine, renowned as one of the oldest and globally consumed alcoholic beverages, possesses a complex composition and aroma shaped by diverse geographical and regulatory influences. To combat fraud in this lucrative industry, the integration of analytical techniques with chemometric tools has become imperative for ensuring authenticity and quality. Scope and approach This review delves into analytical techniques for wine authentication and quality assessment, including chromatography, spectroscopy, and digital imaging, bolstered by chemometrics. It highlights the importance of chemometrics in extracting key statistical insights and understanding the complex attributes of wine. Key findings and conclusions The diverse attributes of wine, ranging from grapevine variety to winemaking techniques, offer opportunities for characterization, classification, and authentication through chemical and physical analyses. Chromatography is the primary analytical method, capable of identifying trace-level organic compounds and detecting adulteration. In addition to chromatographic methods, spectroscopic methods have also found widespread commercial application, often allowing simpler handling compared to chromatography but yielding less extensive data. On the other hand, digital image methods, which have recently begun to be used, represent great potential in wine analysis as they require minimal or no sample preparation. Future readers stand to benefit from a comprehensive understanding of modern wine authentication methods, including chromatography, spectroscopy, digital imaging, and their integration with chemometric tools for innovative analysis approaches.

This study integrates genetic algorithm (GA) with partial least squares regression (PLSR) and various variable selection methods to identify impactful regions of interest (ROI) in heterogeneous 2D chromatogram images for predicting wine age. As wine quality and aroma evolve over time, transitioning from youthful fruitiness to mature, complex flavors, which leads to alterations in the composition of essential aroma-contributing compounds. Chromatograms are segmented into subimages, and the GA-PLSR algorithm optimizes combinations based on grayscale, red-green-blue (RGB), and hue-saturation-value (HSV) histograms. The selected subimage histograms are further refined through interval selection, highlighting the compounds with the most significant influence on wine aging. Experimental validation involving 38 wine samples demonstrates the effectiveness of this approach. Cross-validation reduces the PLS model error from 2.8 to 2.4 years within a 10 × 10 subset, and during prediction, the error decreases from 2.5 to 2.3 years. The study presents a novel approach utilizing the selection of ROI for efficient processing of 2D chromatograms focusing on predicting wine age.

This study aimed to develop a rapid method for the separation of stigmasterol, campesterol and β-sitosterol in Prunus spinosa L. (blackthorn) fruit extracts by HPLC system. Samples were prepared by Soxhlet extraction method and separated on a C18 column using acetonitrile-methanol mobile phase and photodiode array detector (PDA). The optimized method resulted in a linear calibration curve ranging from 1.70–130 μg mL–1 for all three phytosterols. Analyses of external phytosterol standards showed good linearity (R2 of 0.998 to 0.999); LOD and LOQ were determined to be 0.32–9.30 μg mL–1 and 0.98–28.1 μg mL–1, respectively. Repeatability and reproducibility precision analyses showed acceptable values of %RSD. β-sitosterol was the predominant phytosterol (51.53–81.03% of total) among all samples. Method validation parameters indicated that this analytical method can be applied for accurate and precise determination of campesterol, stigmasterol and β-sitosterol, in selected extracts.

Analyzing essential oils is a challenging task for chemists because their composition can vary depending on various factors. The separation potential of volatile compounds using enantioselective two-dimensional gas chromatography coupled with high-resolution time-of-flight mass spectrometry (GC×GC–HRTOF-MS) with three different stationary phases in the first dimension was evaluated to classify different types of rose essential oils. The results showed that selecting only ten specific compounds was enough for efficient sample classification instead of the initial 100 compounds. The study also investigated the separation efficiencies of three stationary phases in the first dimension: Chirasil-Dex, MEGA-DEX DET—β, and Rt-βDEXsp. Chirasil-Dex had the largest separation factor and separation space, ranging from 47.35% to 56.38%, while Rt-βDEXsp had the smallest, ranging from 23.36% to 26.21%. MEGA-DEX DET—β and Chirasil-Dex allowed group-type separation based on factors such as polarity, H-bonding ability, and polarizability, whereas group-type separation with Rt-βDEXsp was almost imperceptible. The modulation period was 6 s with Chirasil-Dex and 8 s with the other two set-ups. Overall, the study showed that analyzing essential oils using GC×GC–HRTOF-MS with a specific selection of compounds and stationary phase can be effective in classifying different oil types.

One of the most effective methods for gaining insight into the composition of trace-level volatile organic characteristics of wine products is through the use of a comprehensive two-dimensional gas chromatography-high resolution mass spectrometry (GC × GC-HRMS) technique. The vast amount of data generated by this method, however, can often be overwhelming requiring exhaustive and time-consuming analysis to identify significant statistical characteristics. The use of advanced chemometric software can achieve the same or even higher efficiency. This study aimed to identify differences based on geographical locations by analyzing the volatile organic compounds in the composition of botrytized wines from Slovakia, Hungary, France, and Austria. The volatile organic compounds were extracted by solid-phase microextraction and analyzed using GC × GC-HRMS. The data obtained from the analysis underwent Fisher-ratio (F-ratio) tile-based analysis to identify statistically significant differences. Principal component analysis demonstrated a significant distinction between wine samples based on geographical location, using only 10 statistically significant features with the highest F-ratio. In the samples, the following compounds were analyzed: methyl-octadecanoate, 2-cyanophenyl-β-phenylpropionate, α-ionone, n-octanoic acid, 1,2-dihydro-1,1,6-trimethyl-naphthalene, methyl-hexadecanoate, ethyl-pentadecanoate, ethyl-decanoate, and γ-nonalactone. These, all play an important role in cluster pattern observed on principal component analysis results. Additionally, hierarchical cluster analysis confirmed this.

The prediction or confirmation of age is an important field in evaluation of wine's value. Such type of studies commonly requires a number of data sets describing changes in chemical composition and/or related physical properties. Digital images of wines captured by a webcam represent an easy and low-cost approach to prevent frauds connected to the age of wines. In this work, a combination of frequency histograms including grey scale, red-green-blue (RGB) and hue-saturation-value (HSV) colour models extracted from digital images were used to evaluate the age of botrytized and related varietal wines produced during a 1989-2019 period at different countries. The main findings showed that digital images carry the appropriate chemical information for the age assignment and PLS-type models were able to estimate wine age only one latent variable. Grey levels enabled to find figure of merit values similar to the PLS model based on full histogram. An additional interval selection of the histograms with interval PLS allows improving accuracy of variable assessment and achieving lower error at a cross-validation step, RMSECV decreases from 3.6 years to 3.1 years. When models were employed to predict an external set of samples similar results were found, RMSEP equal to 2.8 years and 2.9 years for PLS and iPLS, respectively. However, a slight deterioration of the results was observed for the PLS model based on full grey levels (RMSEP 3.2). In general, these non-destructive measurements do not generate residuals and can be performed without sophisticated equipment with a reasonably accurate response.