Drinking water treatment plants (DWTP) use chlorination as an oxidation stage in the first step of the processes used to eliminate the natural organic matter (NOM) responsible for the formation of 2-methylisoborneol (2-MIB) and Geosmin (GM), which produce odor and taste to the water. However, chlorination processes give rise to disinfectant toxic subproducts, such as trihalomethanes (THMs). In this work, a headspace solid-phase microextraction coupled with a gas chromatography/mass spectrometric method has been developed for the quality control of drinking water. 2-MIB, GM, and THMs at different stages of the drinking water treatment process were monitored. The method was validated following the Environmental Protection Agency guidelines. Neither carryover nor the matrix effect was observed. The performance of the method was satisfactory in terms of selectivity, repeatability, and accuracy and exhibited a linear concentration range of 0.8–50 µg/L for trichloromethane (TCM), 0.05–20 µg/L for bromodichloromethane, 0.01–20 µg/L for dibromochloromethane and tribromomethane, and 0.005–0.05 µg/L for GM and 2-MIB. The THMs concentration obtained for all the water samples was below the thresholds established by international organizations and, for 2-MIB and GM, were lower for their limit of quantification. The method was also applied to the adsorption kinetic study of TCM on granulated activated carbon, which is the main barrier to reducing the NOM in DWTP.

The exceptional olfactory capabilities of trained detection dogs demonstrate high potential for identifying infectious diseases. However, safe and standardized canine training requires specific chemical targets rather than infectious biological samples. This study presents an analytical proof-of-concept combining untargeted metabolomics and machine learning (ML) to decode the specific odor profile of SARS-CoV-2 infection. Using headspace solid-phase microextraction gas chromatography coupled with time-of-flight mass spectrometry (HS-SPME-GC/MS-ToF), axillary sweat samples from 76 individuals (SARS-CoV-2 positive and negative) were analyzed. Data preprocessing and dimensionality reduction were performed to feed a Partial Least Squares-Discriminant Analysis (PLS-DA) model. The optimized model achieved an overall accuracy of 79%, with a specificity of 89% and sensitivity of 70% in external validation, identifying a specific panel of Volatile Organic Compounds (VOCs) as discriminant biomarkers. The optimized model achieved robust classification performance, effectively distinguishing infected individuals from healthy controls based solely on their volatilome. Six VOCs were found to be consistently presented in COVID-19-positive individuals. These compounds were proposed as candidate odor signatures for constructing artificial training aids to standardize and accelerate the training of detection dogs. This study establishes a framework where machine learning-driven metabolomic profiling directly informs biological sensor training, offering a novel synergy between ML and biological intelligence in disease detection. This study establishes a scalable computational framework to translate biological samples into chemical data, providing the scientific basis for designing safe, synthetic K9 training aids for future infectious disease outbreaks without the biosafety risks associated with handling live pathogens.

Bacterial cellulose is a high-value biobased material whose production remains constrained by the cost of synthetic media and the limited substrate range of Komagataeibacter xylinus. Here we report a metabolomics investigation combining LC-QTOF-MS profiling of polar metabolites with targeted sugar quantification over seventy days across fourteen fermentation conditions spanning seven media (the synthetic Hestrin-Schramm reference and six agro-industrial waste streams: tea, spent tea leaves, spent coffee grounds, apple, grape, and blueberry waste) inoculated with either K. xylinus alone or a kombucha consortium. Single-strain fermentation supported bacterial cellulose production only in fruit-derived media and Hestrin-Schramm, whereas mixed fermentation enabled production across all substrates, including those where K. xylinus failed entirely. Despite its broader substrate range, the consortium consistently produced lower yields, attributed to competitive consumption of key nitrogen sources by the yeast fraction. Metabolomics revealed that productivity was associated with four sequentially recruited nitrogen tiers: amino acids (week one), purine bases and nucleosides (weeks two to four), methylxanthine alkaloids catabolized via N-demethylation (weeks three onward), and B-vitamin cofactors and modified nucleosides (late phase). The consortium's advantage resided not in greater nitrogen consumption but in chemical niche complementarity: yeasts and bacteria exploited distinct yet complementary nutritional spaces, collectively accessing compound classes unavailable to K. xylinus alone. Carbohydrate dynamics revealed that glucose depletion rate determines fermentation tempo rather than yield and that fructose accumulation reports invertase activity and glucose catabolite repression. These findings support a four-principle framework for rational design of waste-based fermentation media supporting bacterial cellulose production without synthetic nutritional inputs.

Bacterial cellulose is a high-value biobased material whose production remains constrained by the cost of synthetic media and the limited substrate range of Komagataeibacter xylinus. Here, we report a comprehensive metabolomics investigation combining LC-QTOF-MS profiling of polar metabolites with targeted sugar quantification over seventy days across fourteen fermentation conditions, including seven media (synthetic Hestrin-Schramm and six agro-industrial waste streams: tea, spent tea leaves, spent coffee grounds, apple, grape, and blueberry waste) inoculated with either K. xylinus alone or a kombucha consortium. Single-strain fermentation produced bacterial cellulose exclusively in fruit-derived media and Hestrin-Schramm, whereas mixed fermentation enabled production across all substrates. Despite its broader substrate range, the consortium consistently produced lower yields, consistent with competitive consumption of key nitrogen sources by the yeast fraction. Metabolomics indicated four apparent, sequentially recruited nitrogen tiers: amino acids (week one), purine bases and nucleosides (weeks two to four), methylxanthine alkaloids catabolized via N-demethylation (week three onward), and B-vitamin cofactors and modified nucleosides (late phase). The broader substrate range of the consortium was associated not with greater overall nitrogen consumption but with complementary use of the available chemical space, as several compound classes unconsumed under single-strain conditions were depleted during mixed fermentation. Carbohydrate dynamics further showed that glucose depletion rate was not correlated with final yield, while fructose accumulation was consistent with invertase activity and preferential glucose utilization. Based on these findings, we propose a working hypothesis requiring dedicated validation: a four-principle framework for rationally designing glucose-supplemented, waste-based media for bacterial cellulose production.