A B S T R A C TThis study aimed at investigating the chemical composition and in vitro antimicrobial activity of juniper (Juniperus communis L.) berries essential oils (EOs), including commercial samples as well as the oil hydrodistilled from berries grown in Portugal, for which few information is available in the literature. The analysis was performed by gas chromatography coupled to mass spectrometry detection (GC/MS) allowing the identification of a total of 97 compounds. The EOs showed different chemical profiles with only one being according to the European Pharmacopoeia 8 requirements. The laboratory-hydrodistilled EO was characterized by its high content in α-pinene (41.6%), followed by β-pinene (27.6%) and limonene (6.4%), commercial EO1 by α-pinene (31.1%), β-myrcene (16.3%) and sabinene (7.5%) and commercial EO2 by δ-cadinene (16.0%), α-pinene (12.2%) and sabinene (9.4%). The distinct chemical profiles were also evidenced by principal components analysis (PCA), with a clear separation of the evaluated EOs. One of the commercial samples, showed the presence of propachlor, a banned herbicide in the European Union. All the EOs showed relevant antimicrobial activity as they presented microbicidal activity against Candida albicans and at least six of the ten tested bacteria. Commercial EO2 showed a higher biological activity, as it was active against all tested microorganisms, which could be related to its higher content in sesquiterpenes, in particular those oxygenated. Overall, results support the use of Juniper communis L. berries EO as an antiseptic in traditional medicine and highlight its potential as a biopreservative that could be used in different industries.

In the last decade, there has been an increasing demand for wild-captured fish, which attains higher prices compared to farmed species, thus being prone to mislabeling practices. In this work, fatty acid composition coupled to advanced chemometrics was used to discriminate wild from farmed salmon. The lipids extracted from salmon muscles of different production methods and origins (26 wild from Canada, 25 farmed from Canada, 24 farmed from Chile and 25 farmed from Norway) were analyzed by gas chromatography with flame ionization detector (GC-FID). All the tested chemometric approaches, namely principal components analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE) and seven machine learning classifiers, namely k-nearest neighbors (kNN), decision tree, support vector machine (SVM), random forest, artificial neural networks (ANN), naïve Bayes and AdaBoost, allowed for differentiation between farmed and wild salmons using the 17 features obtained from chemical analysis. PCA did not allow clear distinguishing between salmon geographical origin since farmed samples from Canada and Chile overlapped. Nevertheless, using the 17 features in the models, six out of the seven tested machine learning classifiers allowed a classification accuracy of ≥99%, with ANN, naïve Bayes, random forest, SVM and kNN presenting 100% accuracy on the test dataset. The classification models were also assayed using only the best features selected by a reduction algorithm and the best input features mapped by t-SNE. The classifier kNN provided the best discrimination results because it correctly classified all samples according to production method and origin, ultimately using only the three most important features (16:0, 18:2n6c and 20:3n3 + 20:4n6). In general, the classifiers presented good generalization with the herein proposed approach being simple and presenting the advantage of requiring only common equipment existing in most labs.

Methane is a major greenhouse gas and a precursor of tropospheric ozone, and most of its sources are linked to anthropogenic activities. The sources of methane are well known and its monitoring generally involves the use of expensive gas analyzers with high operating costs. Many studies have investigated the use of low-cost gas sensors as an alternative for measuring methane concentrations; however, it is still an area that needs further development to ensure reliable measurements. In this work a low-cost platform for measuring methane within a low concentration range was developed and used in two distinct environments to continuously assess and improve its performance. The methane sensor was the Figaro TGS2600, a metal oxide semiconductor (MOS) based on tin dioxide (SnO2). In a first stage, the monitoring platform was applied in a small ruminant barn after undergoing a multi-point calibration. In a second stage, the system was used in a wastewater treatment plant together with a multi-gas analyzer (Gasera One Pulse). The calibration of low-cost sensor was based on the relation of the readings of the two devices. Temperature and relative humidity were also measured to perform corrections to minimize the effects of these variables on the sensor signal and an active ventilation system was used to improve the performance of the sensor. The system proved to be able to measure low methane concentrations following reliable spatial and temporal patterns in both places. A very similar behavior between both measuring systems was also well noticeable at WWTP. In general, the low-cost system presented good performance under several environmental conditions, showing itself to be a good alternative, at least as a screening monitoring system.

In recent years, there has been an increase in demand for food of animal origin. The number of intensive production systems such as pig and poultry farming has been increasing more and more and exerting great impacts on the environment, due to a large amount of particulate material and gaseous pollutants that are generated within these facilities. Thus, low-cost devices emerge as a cheap alternative that provides farmers with information on indoor air quality in its facilities. However, it is important that these devices make precise and accurate measurements, providing reliable concentration readings. Therefore, the objective of this study is the construction and validation of a low-cost system capable of measuring, storing and sending, via the mobile network, the concentrations of hydrogen sulfide, ammonia, carbon dioxide, PM2.5, PM10, temperature, and relative humidity. Preliminary inter-comparison tests showed that the built system had a reliable behavior in relation to all variables, even though the CO2 sensor was the one with the highest determination coefficient. The built device is able to provide continuous monitoring of atmospheric pollutants concentrations, at low cost and with simple handling.

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