Keeping track of phaeodactylum tricornutum (Bacillariophyta) culture contamination by potentiometric e-tongue

The large-scale cultivation of microalgae provides a wide spectrum of marketable bioproducts, profitably used in many fields, from the preparation of functional health products and feed supplement in aquaculture and animal husbandry to biofuels and green chemistry agents. The commercially successful algal biomass production requires effective strategies to maintain the process at desired productivity and stability levels. Hence, the development of effective early warning methods to timely indicate remedial actions and to undertake countermeasures is extremely important to avoid culture collapse and consequent economic losses. With the aim to develop an early warning method of algal contamination, the potentiometric E-tongue was applied to record the variations in the culture environments, over the whole growth process, of two unialgal cultures, Phaeodactylum tricornutum and a microalgal contaminant, along with those of their mixed culture. The E-tongue system ability to distinguish the cultures and to predict their growth stage, through the application of multivariate data analysis, was shown. A PLS regression method applied to the E-tongue output data allowed a good prediction of culture growth time, expressed as growth days, with R-2 values in a range from 0.913 to 0.960 and RMSEP of 1.97-2.38 days. Moreover, the SIMCA and PLS-DA techniques were useful for cultures contamination monitoring. The constructed PLS-DA model properly discriminated 67% of cultures through the analysis of their growth media, i.e., environments, thus proving the potential of the E-tongue system for a real time monitoring of contamination in microalgal intensive cultivation

Light-activated porphyrinoid-capped nanoparticles for gas sensing

The coupling of semiconductors with organic molecules results in a class of sensors whose chemoresistive properties are dictated by the nature of dyes. Organic molecules generally reduce conductivity, but in the case of optically active dyes, such as porphyrinoids, the conductivity is restored by illumination with visible light. In this paper, we investigated the gas sensing properties of ZnO nanoparticles coated with porphyrins and corroles. Under light illumination, the resistance of these materials increases with the adsorption of volatile compounds but decreases when these are strong electron donors. The behavior of these sensors can be explicated on the basis of the structural difference between free-base porphyrin and corrole, the influence of coordinated metal, and the corresponding electronic structures. These sensors are promising electronic noses that combine the selectivity to strong electron donors with the broad selectivity toward the other classes of chemicals. An efficient representation of the data of this peculiar array can be obtained by replacing the Euclidean distance with the angular distance. To this end, a recently introduced spherical principal component analysis algorithm is applied for the first time to gas sensor array data. Results show that a minimal gas sensor array (four elements) can produce a sort of chemotopic map, which enables us to cluster a very large class of pure chemical vapors. Furthermore, this map provides information about the composition of complex odor matrices, such as the headspaces of beef meat and their evolution over the time

Miniaturized multisensor system with a thermal gradient: Performance beyond the calibration range

Two microchips, each with four identical microstructured sensors using SnO2 nanowires as sensing material (one chip decorated with Ag nanoparticles, the other with Pt nanoparticles), were used as a nano-electronic nose to distinguish five different gases and estimate their concentrations. This innovative approach uses identical sensors working at different operating temperatures thanks to the thermal gradient created by an integrated microheater. A system with in-house developed hardware and software was used to collect signals from the eight sensors and combine them into eight-dimensional data vectors. These vectors were processed with a support vector machine allowing for qualitative and quantitative discrimination of all gases after calibration. The system worked perfectly within the calibrated range (100% correct classification, 6.9% average error on concentration value). This work focuses on minimizing the number of points needed for calibration while maintaining good sensor performance, both for classification and error in estimating concentration. Therefore, the calibration range (in terms of gas concentration) was gradually reduced and further tests were performed with concentrations outside these new reduced limits. Although with only a few training points, down to just two per gas, the system performed well with 96% correct classifications and 31.7% average error for the gases at concentrations up to 25 times higher than its calibration range. At very low concentrations, down to 20 times lower than the calibration range, the system worked less well, with 93% correct classifications and 38.6% average error, probably due to proximity to the limit of detection of the sensors

A potentiometric electronic tongue as a discrimination tool of water-food indicator/contamination bacteria

Microorganism assessment plays a key role in food quality and safety control but conventional techniques are costly and/or time consuming. Alternatively, electronic tongues (E-tongues) can fulfill this critical task. Thus, a potentiometric lab-made E-tongue (40 lipid sensor membranes) was used to differentiate four common food contamination bacteria, including two Gram positive (Enterococcus faecalis, Staphylococcus aureus) and two Gram negative (Escherichia coli, Pseudomonas aeruginosa). Principal component analysis and a linear discriminant analysis-simulated annealing algorithm (LDA-SA) showed that the potentiometric signal profiles acquired during the analysis of aqueous solutions containing known amounts of each studied bacteria allowed a satisfactory differentiation of the four bacterial strains. An E-tongue-LDA-SA model (12 non-redundant sensors) correctly classified 98 ± 5% of the samples (repeated K-fold-CV), the satisfactory performance of which can be attributed to the capability of the lipid membranes to establish electrostatic interactions/hydrogen bonds with hydroxyl, amine and/or carbonyl groups, which are comprised in the bacteria outer membranes. Furthermore, multiple linear regression models, based on selected subsets of E-tongue sensors (1215 sensors), also allowed quantifying the bacteria contents in aqueous solutions (0.993 ± 0.011 R2 0.998 ± 0.005, for repeated K-fold-CV). In conclusion, the E-tongue could be of great value as a preliminary food quality and safety diagnosis tool.Foundation for Science and Technology (FCT, Portugal) for financial support by national funds FCT/MCTES to CIMO (UIDB/00690/2020) and to CEB (UIDB/04469/2020), as well as to the BioTecNorte operation (NORTE-01-0145-FEDER-000004) funded by the European Regional Development Fund under the scope of Norte2020—Programa Operacional Regional do Norte. Ítala M.G. Marx also acknowledges the Ph.D. research grant (SFRH/BD/137283/2018) provided by FCT.info:eu-repo/semantics/publishedVersio

Microorganisms’ discrimination using an electronic nose-chemometric approach

Mestrado de dupla diplomação com a Université Libre de TunisThe detection/identification of microorganisms is of major relevance for food quality and safety. Traditional analytical procedures (e.g., culture methods, immunological techniques, and polymerase chain reaction), while accurate and widely used, are time-consuming, costly, and generate a large amount of waste. Sensor-based instruments have evolved as quicker and sensitive complementary identification tools for yeasts, bacteria and fungi. Electronic noses (E-noses), in combination with chemometrics, have been effectively employed for the detection/discrimination of different microorganisms, providing a green, quick, cost-effective, and non-destructive/non-invasive assessment. The successful use of the E-noses may be related to the generation of distinctive olfactory fingerprints of certain volatile organic compounds (VOCs) during the microorganism's growth. These devices have already been used to detect/discriminate fungi and bacteria (e.g.,Enterococcus faecalis, Escherichia coli, Klebsiella pneumonia, Listeria monocytogenes, Pseudomonas aeruginosa), namely in milk, juice, soups,goat and pork meat,fruits and vegetables. Thus, a lab-made E-nose, with nine metal oxide semiconductor sensors, was applied to detect, differentiate, andquantify four common food contamination/quality indicator bacteria, including two Gram positive (E. faecalisand S. aureus) and two Gram negative (E. coli and P. aeruginosa). Besides, to support the E-nose performance the volatile profiles generated by these bacteria were also assessed by headspace solid-phase micro extraction gas-chromatography-mass spectrometry. The volatile profiles comprised 15 identified VOCs, being 10 of them emitted by at least one of the four bacteria evaluated, namely two alcohols (1-butanol, and 1-nonanol), three pyrazines (2-ethyl-6-methyl-pyrazine, 3-ethyl-2,5-dimethylpyrazine,and trimethylpyrazine), three terpenes (camphene, D-limonene, and ƒÒ-pinene), and two other compounds (2,4-thujadiene and indole). The four bacteria could be distinguished using the electrical resistance signals produced by the E-nose in combination with linear discriminate analysis (90% of correct classifications for leave-one-out cross-validation). Additionally, multiple linear regression models, with root mean square errors lower than 4 colony forming units, were successfully established (0.9428 . R2.0.9946). Overall, the E-nose proved to be an effective qualitative-quantitative tool for analyzing bacteria in solid matrices, being foreseen it possible application to solid food matrices.A deteccao/identificacao de microorganismos e de grande relevancia para a qualidade e seguranca dos alimentos. Os procedimentos analiticos tradicionais (por exemplo, metodos de cultura, tecnicas imunologicas e reacao em cadeia de polimerase), embora precisos e amplamente utilizados, sao demorados, dispendiosos e geram uma grande quantidade de residuos. Os instrumentos baseados em sensores evoluiram como ferramentas de identificacao complementares mais rapidas e sensiveis para leveduras, bacterias e fungos. Os narizes eletronicos, em combinacao com a quimetria, foram efetivamente empregados para a deteccao/discriminacao de diferentes microorganismos, proporcionando uma avaliacao verde, rapida, economica e nao destrutiva/nao invasiva. O uso bem-sucedido dos E-noses pode estar relacionado a geracao de impressoes olfativas distintivas de certos compostos organicos volateis (VOCs) durante o crescimento do microorganismo. Estes dispositivos ja foram usados para detectar/discriminar fungos e bacterias (por exemplo, Enterococcus faecalis, Escherichia coli, Klebsiella pneumonia, Listeria monocytogenes, Pseudomonas aeruginosa), nomeadamente no leite, suco, sopas, carne de cabra e de porco, frutas e legumes. Assim, um E-nose feito em laboratorio, com nove sensores semicondutores de oxido de metal, foi aplicado para detectar, diferenciar e quantificar quatro bacterias comuns de contaminacao alimentar / indicador de qualidade, incluindo duas Gram positivas (E. faecalis e S. aureus) e duas Gram negativas. (E. coli and P. aeruginosa). Alem disso, para apoiar o desempenho do nariz E, os perfis volateis gerados por essas bacterias tambem foram avaliados por micro-espectrometria de extraccao de gas-cromatografia-massa de fase solida.Os perfis Volateis consistiam em 15 VOCs identificados, sendo 10 deles emitidos por pelo menos uma das quatro bacterias avaliadas, ou seja, dois alcoois (1-butanol e 1-nonanol), tres pirazinas (2-etil-6-metil-pirazina, 3-etil-2,5-dimetilpyrazina, e trimethylpyrazine), tres terpenos (hcampene, D-limonene e-pinene), e outros dois compostos. (2,4-thujadiene and indole). As quatro bacterias poderiam ser distinguidas usando os sinais de resistencia eletrica produzidos pelo E-nose em combinacao com a analise discriminante linear (90% das classificacoes corretas para a validacao cruzada de abandono-um-out). Alem disso, foram estabelecidos com sucesso multiplos modelos de regressao linear, com erros medios do quadrado raiz inferiores a 4 unidades de formacao de colonia (0,9428 . R2.0,9946). No geral, o E-nose provou ser uma ferramenta qualitativa-quantitativa eficaz para analisar bacterias em matrizes solidas, prevendo-se a possivel aplicacao a matrizs de alimentos solidos

Paving the Way for a Green Transition in the Design of Sensors and Biosensors for the Detection of Volatile Organic Compounds (VOCs)

The efficient and selective detection of volatile organic compounds (VOCs) provides key information for various purposes ranging from the toxicological analysis of indoor/outdoor environments to the diagnosis of diseases or to the investigation of biological processes. In the last decade, different sensors and biosensors providing reliable, rapid, and economic responses in the detection of VOCs have been successfully conceived and applied in numerous practical cases; however, the global necessity of a sustainable development, has driven the design of devices for the detection of VOCs to greener methods. In this review, the most recent and innovative VOC sensors and biosensors with sustainable features are presented. The sensors are grouped into three of the main industrial sectors of daily life, including environmental analysis, highly important for toxicity issues, food packaging tools, especially aimed at avoiding the spoilage of meat and fish, and the diagnosis of diseases, crucial for the early detection of relevant pathological conditions such as cancer and diabetes. The research outcomes presented in the review underly the necessity of preparing sensors with higher efficiency, lower detection limits, improved selectivity, and enhanced sustainable characteristics to fully address the sustainable manufacturing of VOC sensors and biosensors

Biosensors for Diagnosis and Monitoring

Biosensor technologies have received a great amount of interest in recent decades, and this has especially been the case in recent years due to the health alert caused by the COVID-19 pandemic. The sensor platform market has grown in recent decades, and the COVID-19 outbreak has led to an increase in the demand for home diagnostics and point-of-care systems. With the evolution of biosensor technology towards portable platforms with a lower cost on-site analysis and a rapid selective and sensitive response, a larger market has opened up for this technology. The evolution of biosensor systems has the opportunity to change classic analysis towards real-time and in situ detection systems, with platforms such as point-of-care and wearables as well as implantable sensors to decentralize chemical and biological analysis, thus reducing industrial and medical costs. This book is dedicated to all the research related to biosensor technologies. Reviews, perspective articles, and research articles in different biosensing areas such as wearable sensors, point-of-care platforms, and pathogen detection for biomedical applications as well as environmental monitoring will introduce the reader to these relevant topics. This book is aimed at scientists and professionals working in the field of biosensors and also provides essential knowledge for students who want to enter the field

Aspergillus species discrimination using a gas sensor array

The efficiency of electronic noses in detecting and identifying microorganisms has been proven by several studies. Since volatile compounds change with the growth of colonies, the identification of strains is highly dependent on the growing conditions. In this paper, the effects of growth were investigated with different species ofAspergillus, which is one of the most studied microorganisms because of its implications in environmental and food safety. For this purpose, we used an electronic nose previously utilized for volatilome detection applications and based on eight porphyrins-functionalized quartz microbalances. The volatile organic compounds (VOCs) released by cultured fungi were measured at 3, 5, and 10 days after the incubation. The signals from the sensors showed that the pattern of VOCs evolve with time. In particular, the separation between the three studied strains progressively decreases with time. The three strains could still be identified despite the influence of culture time. Linear Discriminant Analysis (LDA) showed an overall accuracy of 88% and 71% in the training and test sets, respectively. These results indicate that the presence of microorganisms is detectable with respect to background, however, the difference between the strains changes with the incubation time

Aspergillus Species Discrimination Using a Gas Sensor Array

The efficiency of electronic noses in detecting and identifying microorganisms has been proven by several studies. Since volatile compounds change with the growth of colonies, the identification of strains is highly dependent on the growing conditions. In this paper, the effects of growth were investigated with different species of Aspergillus, which is one of the most studied microorganisms because of its implications in environmental and food safety. For this purpose, we used an electronic nose previously utilized for volatilome detection applications and based on eight porphyrins-functionalized quartz microbalances. The volatile organic compounds (VOCs) released by cultured fungi were measured at 3, 5, and 10 days after the incubation. The signals from the sensors showed that the pattern of VOCs evolve with time. In particular, the separation between the three studied strains progressively decreases with time. The three strains could still be identified despite the influence of culture time. Linear Discriminant Analysis (LDA) showed an overall accuracy of 88% and 71% in the training and test sets, respectively. These results indicate that the presence of microorganisms is detectable with respect to background, however, the difference between the strains changes with the incubation time