Automatic learning for the classification of chemical reactions and in statistical thermodynamics

This Thesis describes the application of automatic learning methods for a) the classification of organic and metabolic reactions, and b) the mapping of Potential Energy Surfaces(PES). The classification of reactions was approached with two distinct methodologies: a representation of chemical reactions based on NMR data, and a representation of chemical reactions from the reaction equation based on the physico-chemical and topological features of chemical bonds. NMR-based classification of photochemical and enzymatic reactions. Photochemical and metabolic reactions were classified by Kohonen Self-Organizing Maps (Kohonen SOMs) and Random Forests (RFs) taking as input the difference between the 1H NMR spectra of the products and the reactants. The development of such a representation can be applied in automatic analysis of changes in the 1H NMR spectrum of a mixture and their interpretation in terms of the chemical reactions taking place. Examples of possible applications are the monitoring of reaction processes, evaluation of the stability of chemicals, or even the interpretation of metabonomic data. A Kohonen SOM trained with a data set of metabolic reactions catalysed by transferases was able to correctly classify 75% of an independent test set in terms of the EC number subclass. Random Forests improved the correct predictions to 79%. With photochemical reactions classified into 7 groups, an independent test set was classified with 86-93% accuracy. The data set of photochemical reactions was also used to simulate mixtures with two reactions occurring simultaneously. Kohonen SOMs and Feed-Forward Neural Networks (FFNNs) were trained to classify the reactions occurring in a mixture based on the 1H NMR spectra of the products and reactants. Kohonen SOMs allowed the correct assignment of 53-63% of the mixtures (in a test set). Counter-Propagation Neural Networks (CPNNs) gave origin to similar results. The use of supervised learning techniques allowed an improvement in the results. They were improved to 77% of correct assignments when an ensemble of ten FFNNs were used and to 80% when Random Forests were used. This study was performed with NMR data simulated from the molecular structure by the SPINUS program. In the design of one test set, simulated data was combined with experimental data. The results support the proposal of linking databases of chemical reactions to experimental or simulated NMR data for automatic classification of reactions and mixtures of reactions. Genome-scale classification of enzymatic reactions from their reaction equation. The MOLMAP descriptor relies on a Kohonen SOM that defines types of bonds on the basis of their physico-chemical and topological properties. The MOLMAP descriptor of a molecule represents the types of bonds available in that molecule. The MOLMAP descriptor of a reaction is defined as the difference between the MOLMAPs of the products and the reactants, and numerically encodes the pattern of bonds that are broken, changed, and made during a chemical reaction. The automatic perception of chemical similarities between metabolic reactions is required for a variety of applications ranging from the computer validation of classification systems, genome-scale reconstruction (or comparison) of metabolic pathways, to the classification of enzymatic mechanisms. Catalytic functions of proteins are generally described by the EC numbers that are simultaneously employed as identifiers of reactions, enzymes, and enzyme genes, thus linking metabolic and genomic information. Different methods should be available to automatically compare metabolic reactions and for the automatic assignment of EC numbers to reactions still not officially classified. In this study, the genome-scale data set of enzymatic reactions available in the KEGG database was encoded by the MOLMAP descriptors, and was submitted to Kohonen SOMs to compare the resulting map with the official EC number classification, to explore the possibility of predicting EC numbers from the reaction equation, and to assess the internal consistency of the EC classification at the class level. A general agreement with the EC classification was observed, i.e. a relationship between the similarity of MOLMAPs and the similarity of EC numbers. At the same time, MOLMAPs were able to discriminate between EC sub-subclasses. EC numbers could be assigned at the class, subclass, and sub-subclass levels with accuracies up to 92%, 80%, and 70% for independent test sets. The correspondence between chemical similarity of metabolic reactions and their MOLMAP descriptors was applied to the identification of a number of reactions mapped into the same neuron but belonging to different EC classes, which demonstrated the ability of the MOLMAP/SOM approach to verify the internal consistency of classifications in databases of metabolic reactions. RFs were also used to assign the four levels of the EC hierarchy from the reaction equation. EC numbers were correctly assigned in 95%, 90%, 85% and 86% of the cases (for independent test sets) at the class, subclass, sub-subclass and full EC number level,respectively. Experiments for the classification of reactions from the main reactants and products were performed with RFs - EC numbers were assigned at the class, subclass and sub-subclass level with accuracies of 78%, 74% and 63%, respectively. In the course of the experiments with metabolic reactions we suggested that the MOLMAP / SOM concept could be extended to the representation of other levels of metabolic information such as metabolic pathways. Following the MOLMAP idea, the pattern of neurons activated by the reactions of a metabolic pathway is a representation of the reactions involved in that pathway - a descriptor of the metabolic pathway. This reasoning enabled the comparison of different pathways, the automatic classification of pathways, and a classification of organisms based on their biochemical machinery. The three levels of classification (from bonds to metabolic pathways) allowed to map and perceive chemical similarities between metabolic pathways even for pathways of different types of metabolism and pathways that do not share similarities in terms of EC numbers. Mapping of PES by neural networks (NNs). In a first series of experiments, ensembles of Feed-Forward NNs (EnsFFNNs) and Associative Neural Networks (ASNNs) were trained to reproduce PES represented by the Lennard-Jones (LJ) analytical potential function. The accuracy of the method was assessed by comparing the results of molecular dynamics simulations (thermal, structural, and dynamic properties) obtained from the NNs-PES and from the LJ function. The results indicated that for LJ-type potentials, NNs can be trained to generate accurate PES to be used in molecular simulations. EnsFFNNs and ASNNs gave better results than single FFNNs. A remarkable ability of the NNs models to interpolate between distant curves and accurately reproduce potentials to be used in molecular simulations is shown. The purpose of the first study was to systematically analyse the accuracy of different NNs. Our main motivation, however, is reflected in the next study: the mapping of multidimensional PES by NNs to simulate, by Molecular Dynamics or Monte Carlo, the adsorption and self-assembly of solvated organic molecules on noble-metal electrodes. Indeed, for such complex and heterogeneous systems the development of suitable analytical functions that fit quantum mechanical interaction energies is a non-trivial or even impossible task. The data consisted of energy values, from Density Functional Theory (DFT) calculations, at different distances, for several molecular orientations and three electrode adsorption sites. The results indicate that NNs require a data set large enough to cover well the diversity of possible interaction sites, distances, and orientations. NNs trained with such data sets can perform equally well or even better than analytical functions. Therefore, they can be used in molecular simulations, particularly for the ethanol/Au (111) interface which is the case studied in the present Thesis. Once properly trained, the networks are able to produce, as output, any required number of energy points for accurate interpolations

Prediction of protein-ligand binding affinity using neural networks

Master'sMASTER OF SCIENC

Development of High Resolution Mass Spectrometric Methods for the investigation of food authenticity

Η αυθεντικότητα των τροφίμων αποτελεί ένα ιδαίτερα σημαντικό γεγονός τα τελευταία χρόνια, λόγω των πολλών περιστατικών νοθείας τροφίμων και διακίνησης προϊόντων κατώτερης ποιότητας με παραπλανητικές ετικέτες. Η εξασφάλιση της αυθεντικότητας του ελαιολάδου δημιουργεί μεγάλη ανησυχία λόγω της οργανοληπτικής, θρεπτικής και οικονομικής του σημασίας. Ο κύριος στόχος αυτής της διατριβής είναι η ανάπτυξη αναλυτικών μεθόδων υγροχρωματογραφίας φασματομετρίας μάζας υψηλής διακριτικής ικανότητας (LC-HRMS) που θα συμπεριλαμβάνουν στρατηγικές στοχευμένης, «ύποπτης» και «μη στοχευμένης σάρωσης», σε συνδυασμό με προηγμένα χημειομετρικά εργαλεία, για την εύρεση του αποτυπώματος του ελαιολάδου. Aρχικά, αναφέρονται οι βιολογικές δράσεις των ελάσσονων συστατικών του ελαιολάδου, των φαινολικών ενώσεων, και οι επιδράσεις διαφόρων παραγόντων στο φαινολικό προφίλ. Παρουσιάζονται όλες οι σύγχρονες και αναλυτικές μέθοδοι που συνδυάζουν την επιστήμη των τροφίμων με τεχνικές μεταβολομικής, με έμφαση στις μεθόδους HR-MS. Το πειραματικό μέρος της διατριβής αποτελείται από τρία τμήματα: (1) Μελέτες αυθεντικότητας ελαιολάδου με LC-QTOF-MS σε συνδυασμό με στοχευμένη και μη στοχευμένη σάρωση και χημειομετρία για την αναγνώριση δεικτών που κατατάσσουν το ελαιόλαδο ως ελαττωματικό ή έξτρα παρθένο. (Κεφάλαιο 3), (2) Διερεύνηση του βιολογικού και συμβατικού τύπου παραγωγής των έξτρα παρθένων ελαιολάδων με LC-QTOF-MS σε συνδυασμό με στοχευμένη και «ύποπτη» σάρωση, προτείνοντας μια νέα μέθοδο ημι-ποσοτικοποίησης (Κεφάλαιο 4), και (3) Ταξινόμηση έξι ελληνικών ποικιλιών ελαιoλάδου με LC-QTOF-MS μη στοχευμένη στοχευμένη σάρωση και χημειομετρία, για την αναγνώριση δεικτών με προκαθορισμένα όρια συγκέντρωσης, που εγγυώνται την ποικιλιακή και γεωγραφική προέλευση (Κεφάλαιο 5). Πιστεύουμε ότι οι παραπάνω μελέτες έχουν σημειώσει μεγάλη πρόοδο στον τομέα της γνησιότητας των τροφίμων με την ανάπτυξη αναλυτικών μεθόδων HR-MS και μέσω της εισαγωγής μιας νέας ολοκληρωμένης ροής εργασιών που περιλαμβάνει στρατηγικές στοχευμένης, «ύποπτης» και μη στοχευμένης σάρωσης HRMS, σε συνδυασμό με, ολοκληρωμένα εργαλεία εξόρυξης, επεξεργασίας δεδομένων και προγνωστικά μοντέλα.Food authenticity has become increasingly important in recent years due to food fraud incidents and the handling of low quality products with misleading labels. The guarantee of the authenticity of olive oil arises great public concern because of its sensory, nutritional and economic importance. The main objective of this thesis is to develop integrated LC-HRMS workflows, including target, suspect and non-target screening strategies, coupled with advanced chemometric tools, for olive oil fingerprinting. First, the biological activities of some minor constituents in olive oil, phenolic compounds, are reported and their occurrence and wide-scope properties in olive matrices, as well as effects of various factors on olive oil phenolic profile, are discussed. The present state of the art for their determination in foodomics science is presented, focusing on target and non-target screening HR-MS workflows coupled to chemometrics. The experimental section of the thesis consists of three sections: (1) Olive oil authenticity studies by target and non-target LC-QTOF-MS combined with advanced chemometrics, for identifying markers that classify olive oil to defective and EVOOs (Chapter 3), (2) Investigating the organic and conventional production type of EVOOs with target and suspect screening by LC-QTOF-MS, a novel semi-quantification method using chemical similarity and advanced chemometris; in order to identify a marker with a concentration threshold, by ACO/RF, that can be used to discriminate organic and conventional EVOOs (Chapter 4), and (3) Classification of Greek olive oil varieties with non-target UHPLC-QTOF-MS and advanced chemometrics; for the investigation of the fingerprints of six greek olive oil varieties and the identification of markers, with post-defined concentration thresholds, that guarantee the varietal and geographical origin (Chapter 5). We believe that these studies have made great progress in the food authenticity field via the introduction of novel integrated HRMS screening workflows which are followed by advanced data processing, comprehensive data mining and predictive modelling tools

Artificial intelligence applications in waste water monitoring for industrial purposes

This thesis reports on work carried out in the development of software for artificial intelligence sensing systems based on UV-Vis spectroscopy, designed for remote on-line and real-time analysis for monitoring of industrial effluent. A feasibility study on artificial intelligence methods and the design of an intelligent monitoring system has been researched. This system is capable of detecting the occurrences of chemical pollutants and the concentration of species involved. The controlling software was developed in this work for the remote modem control of a computer controlled UV-Vis spectrometer system. This provides facilities for signal processing, data storage, and transfer of data to a host machine for real-time analysis. This gives significant advantages in term of automatically and instantly reporting of a pollution incident. This front end sensing system has been installed at industrial sites in order to demonstrate the apparatus in the real situations and to obtain data for qualitative analysis. Difficulties in working with the above data pointed to the need for a laboratory-based evaluation and modelling of analysis methods. This evaluation and development of suitable methods forms a major part of the work. The samples prepared for a set of data were mixtures of nitrate, hypochlorite and ammonia in various concentrations representatives of that expected in real outflows. This data set presented several significant problems in data analysis, including an overlap of UV absorption bands and the interaction between ammonia and hypochlorite to form monochloramine which has its own specific spectral features. In the evaluation, the spectral data obtained were analysed by two different methods. The first was Principle Component Analysis (PCA) which is based on linear multivariate analysis, and samples were investigated to compare the effects of interactions between components. The second method was Neural Network analysis, which is a non-linear analysis technique. After considerable effort, this approach resulted in a data analysis scheme where the Back-Propagation algorithm was used as a two-step process. In the first-step, the network inputs were derived by binary encoding segments of the second derivative of the absorption spectra according to their shape and the network outputs specified according to which species were likely to occur. As a result, the second-step network could then focus on a few inputs that strongly correlate with the presence of the expected species. Also the second-step provided a filter that compensated for false classification of species, at low concentration levels. The resulting data analysis scheme depends on a knowledge of the expected chemistry for implementation: however it gives a much better performance than PCA in this particular case. The complete monitoring system has been integrated with a Graphical User Interface software to perform real time analysis at a host machine. A multi-task system for on-line monitoring of data transmitted from a remote site, has been developed, based on the neural network approach. Finally, the intelligent monitoring system is demonstrated and evaluated

Atmospheric particulate matter characterization by Fourier transform infrared spectroscopy: a review of statistical calibration strategies for carbonaceous aerosol quantification in US measurement networks

Atmospheric particulate matter (PM) is a complex mixture of many different substances and requires a suite of instruments for chemical characterization. Fourier transform infrared (FT-IR) spectroscopy is a technique that can provide quantification of multiple species provided that accurate calibration models can be constructed to interpret the acquired spectra. In this capacity, FT-IR spectroscopy has enjoyed a long history in monitoring gas-phase constituents in the atmosphere and in stack emissions. However, application to PM poses a different set of challenges as the condensed-phase spectrum has broad, overlapping absorption peaks and contributions of scattering to the mid-infrared spectrum. Past approaches have used laboratory standards to build calibration models for prediction of inorganic substances or organic functional groups and predict their concentration in atmospheric PM mixtures by extrapolation. In this work, we review recent studies pursuing an alternate strategy, which is to build statistical calibration models for mid-IR spectra of PM using collocated ambient measurements. Focusing on calibrations with organic carbon (OC) and elemental carbon (EC) reported from thermal–optical reflectance (TOR), this synthesis serves to consolidate our knowledge for extending FT-IR spectroscopy to provide TOR-equivalent OC and EC measurements to new PM samples when TOR measurements are not available. We summarize methods for model specification, calibration sample selection, and model evaluation for these substances at several sites in two US national monitoring networks: seven sites in the Interagency Monitoring of Protected Visual Environments (IMPROVE) network for the year 2011 and 10 sites in the Chemical Speciation Network (CSN) for the year 2013. We then describe application of the model in an operational context for the IMPROVE network for samples collected in 2013 at six of the same sites as in 2011 and 11 additional sites. In addition to extending the evaluation to samples from a different year and different sites, we describe strategies for error anticipation due to precision and biases from the calibration model to assess model applicability for new spectra a priori. We conclude with a discussion regarding past work and future strategies for recalibration. In addition to targeting numerical accuracy, we encourage model interpretation to facilitate understanding of the underlying structural composition related to operationally defined quantities of TOR OC and EC from the vibrational modes in mid-IR deemed most informative for calibration. The paper is structured such that the life cycle of a statistical calibration model for FT-IR spectroscopy can be envisioned for any substance with IR-active vibrational modes, and more generally for instruments requiring ambient calibrations.</p

Atmospheric particulate matter characterization by Fourier transform infrared spectroscopy: a review of statistical calibration strategies for carbonaceous aerosol quantification in US measurement networks

Atmospheric particulate matter (PM) is a complex mixture of many different substances and requires a suite of instruments for chemical characterization. Fourier transform infrared (FT-IR) spectroscopy is a technique that can provide quantification of multiple species provided that accurate calibration models can be constructed to interpret the acquired spectra. In this capacity, FT-IR spectroscopy has enjoyed a long history in monitoring gas-phase constituents in the atmosphere and in stack emissions. However, application to PM poses a different set of challenges as the condensed-phase spectrum has broad, overlapping absorption peaks and contributions of scattering to the mid-infrared spectrum. Past approaches have used laboratory standards to build calibration models for prediction of inorganic substances or organic functional groups and predict their concentration in atmospheric PM mixtures by extrapolation. In this work, we review recent studies pursuing an alternate strategy, which is to build statistical calibration models for mid-IR spectra of PM using collocated ambient measurements. Focusing on calibrations with organic carbon (OC) and elemental carbon (EC) reported from thermal-optical reflectance (TOR), this synthesis serves to consolidate our knowledge for extending FT-IR spectroscopy to provide TOR-equivalent OC and EC measurements to new PM samples when TOR measurements are not available. We summarize methods for model specification, calibration sample selection, and model evaluation for these substances at several sites in two US national monitoring networks: seven sites in the Interagency Monitoring of Protected Visual Environments (IMPROVE) network for the year 2011 and 10 sites in the Chemical Speciation Network (CSN) for the year 2013. We then describe application of the model in an operational context for the IMPROVE network for samples collected in 2013 at six of the same sites as in 2011 and 11 additional sites. In addition to extending the evaluation to samples from a different year and different sites, we describe strategies for error anticipation due to precision and biases from the calibration model to assess model applicability for new spectra a priori. We conclude with a discussion regarding past work and future strategies for recalibration. In addition to targeting numerical accuracy, we encourage model interpretation to facilitate understanding of the underlying structural composition related to operationally defined quantities of TOR OC and EC from the vibrational modes in mid-IR deemed most informative for calibration. The paper is structured such that the life cycle of a statistical calibration model for FT-IR spectroscopy can be envisioned for any substance with IR-active vibrational modes, and more generally for instruments requiring ambient calibrations

Profiling and modelling of triglycerides and volatile compounds in SA hake (merluccius capensis and merluccius paradoxus)

Thesis (D. Tech. Environmental health) -- Central University of technology, Free State, 2011Apart from being the primary food source of many cultures around the world, fish contains notable amounts of essential fatty acids that are required by the human body, thus making fish a vital part of the human diet. In South Africa Cape hake is a well-known and highly consumed local fish species, which is transported from coastal areas countrywide where the fresh fish are displayed on ice in various retail stores. Fish is known to be highly susceptible to spoilage and, as a result, the maintenance of the cold-chain in related products is of particular importance. Additionally, recent trends showing a decline in natural fish resources have instigated growing concerns about the sustainability and optimal utilisation of fish as a food source. Against this backdrop, this study aimed at determining the influence of storage parameters on selected triglycerides and their possible metabolic pathways. Also applying prediction modelling of fatty acids and volatiles as instruments to assess exposure of Cape hake fillets to excessive microbial contamination and, in effect, be indicative of the environmental parameters (for example temperature) that may influence such contamination. Randomly selected juvenile hakes were filleted and stored under various simulated retail storage conditions, under either controlled or uncontrolled environmental conditions. For each hake filleted, one fillet was inoculated with an increased load of autochthonous microbiota, and the corresponding fillet was kept at similar temperature conditions. All fillets were monitored over a ten day period, during which fatty acid and volatile samples were collected and analysed. From the resulting triglycerides a selection of fatty acids were profiled and their possible metabolic pathways investigated. Fish maturity, the distribution of the fatty acids and the implication thereof in the nutritional value were also assessed. Conventional chemometric methods utilising mathematical expressions were subsequently utilised in order to predict contamination and whether the cold chain was sustained, while an artificial neural network (ANNs) were designed to predict excessive microbial contamination in the fillets. The results showed that the nutritional value of fish differs notably with its maturity and size. Mathematical equations were furthermore found to be effective assessment instruments to indicate the percentage differences in storage temperature, as well as consequent microbial influences. Thus, this approach may introduce mathematical prediction modelling as a promising mechanism to assess Cape hake spoilage. An artificial neural network (ANN) was successfully designed, that succeeded in distinguishing between Cape hake fillets displayed and stored on ice that have been exposed to excessive contamination and those that have not been exposed. In the latter case, the selected variable was a fatty acid, hexadecanoic acid, used as biochemical indicator. This modulating approach may provide a platform for future shelf-life studies on related muscle tissue. Ultimately, the study endeavoured to add to the body of knowledge regarding the biochemical and microbiological changes related to Cape hake storage, the prediction thereof via contemporary methods and contributing to the safety and effective utilization of this unique and declining South African nutritional resource

Development of quantitative structure property relationships to support non-target LC-HRMS screening

Κατά την τελευταία δεκαετία, ένας μεγάλος αριθμός αναδυόμενων ρύπων έχουν ανιχνευθεί και ταυτοποιηθεί σε επιφανειακά ύδατα και λύματα, προκαλώντας ανησυχία για το υδάτινο οικοσύστημα, λόγω της πιθανής χημικής τους σταθερότητας. Η τεχνική της υγροχρωματογραφίας - φασματομετρίας μάζας υψηλής διακριτικής ικανότητας (LC-HRMS) αποτελεί μια αποτελεσματική τεχνική για την ανίχνευση αναδυόμενων ρύπων στο περιβάλλον. Η ταυτόχρονη δε ανάλυση των δειγμάτων με τις συμπληρωματικές τεχνικές της υγροχρωματογραφίας αντίστροφης φάσης (RPLC) και της υγροχρωματογραφίας υδρόφιλων αλληλεπιδράσεων (HILIC), συντελεί στην ταυτοποίηση «ύποπτων» ή και άγνωστων ρύπων με ποικίλες φυσικοχημικές ιδιότητες. Για την ταυτοποίηση τους, απαιτείται να πληρούνται συγκεκριμένα κριτήρια, τα οποία αξιολογούνται με βάση τη χρήση διαγνωστικών εργαλείων, όπως η ακριβής πρόβλεψη του χρόνου ανάσχεσης, η in silico θραυσματοποίηση και η πρόβλεψη της συμπεριφορά τους στον ιοντισμό. Στο 3ο κεφάλαιο της παρούσας διδακτορικής διατριβής περιγράφεται η ανάπτυξη μιας ολοκληρωμένης πορείας εργασίας (workflow) για τη διερεύνηση των παραμέτρων που επηρεάζουν τον χρόνο έκλουσης μεγάλου αριθμού ενώσεων που συγκαταλέγονται στους αναδυόμενους ρύπους. Για τον σκοπό αυτό, πάνω από 2.500 αναδυόμενοι ρύποι χρησιμοποιήθηκαν για την ανάπτυξη του μοντέλου πρόβλεψης χρόνου ανάσχεσης για τις 2 υγροχρωματογραφικές τεχνικές (RP- και HILIC-LC-HRMS) και για ηλεκτροψεκασμό τόσο σε θετικό όσο και σε αρνητικό ιοντισμό (+/-ESI). Στη συνέχεια, πραγματοποιήθηκε εφαρμογή του μοντέλου για την υπολογιστική πρόβλεψη του χρόνου ανάσχεσης, για την ταυτοποίηση 10 νέων προϊόντων μετασχματισμού των φαρμακευτικών ενώσεων (tramadol, furosemide και niflumic acid) ύστερα από επεξεργασία με όζον. Στο 4ο κεφάλαιο παρουσιάζεται η ανάπτυξη ενός καινοτόμου γενικευμένου χημειομετρικού μοντέλου το οποίο είναι ικανό να προβλέπει τον χρόνο έκλουσης κάθε πιθανού ρύπου, ανεξαρτήτου υγροχρωματογραφικής μεθόδου που χρησιμοποιείται, συμβάλλοντας σημαντικά στην σύγκριση αποτελεσμάτων από διαφορετικές LC-HRMS μεθόδους. Το συγκεκριμένο μοντέλο χρησιμοποιήθηκε για την ταυτοποίηση «ύποπτων» και άγνωστων ενώσεων σε διεργαστηριακές δοκιμές. Το Κεφάλαιο 5, περιέχει την περιγραφή της ανάπτυξης ενός υπολογιστικού μοντέλου πρόβλεψης τοξικότητας αναδυόμενων ρύπων που ανιχνεύονται στο υδάτινο οικοσύστημα. Το συγκεκριμένο μοντέλο αποσκοπεί στην εκτίμηση του πιθανού περιβαλλοντικού κινδύνου για νέες ενώσεις που ταυτοποιήθηκαν μέσω σάρωσης «ύποπτων» ενώσεων και μη-στοχευμένης σάρωσης, για τις οποίες δεν είναι ακόμα διαθέσιμα πειραματικά δεδομένα τοξικότητας. Τέλος, στο κεφάλαιο 6 παρουσιάζεται ένας αυτοματοποιημένος και συστηματικός τρόπος σάρωσης «ύποπτων» ενώσεων και μη-στοχευμένης σάρωσης σε δεδομένα από LC-HRMS. Η νέα αυτή αυτοματοποιημένη πορεία εργασίας, αποσκοπεί στην λιγότερο χρονοβόρα επεξεργασία των HRMS δεδομένων, και στην εφαρμογή της μη-στοχευμένης σάρωσης ώστε να είναι δυνατή η εφαρμογή τους σε καθημερινούς ελέγχους ρουτίνας ή/και για χρήση από τις κανονιστικές αρχές.Over the last decade, a high number of emerging contaminants were detected and identified in surface and waste waters that could threaten the aquatic environment due to their pseudo-persistence. As it is described in chapters 1 and 2, liquid chromatography high resolution mass spectroscopy (LC-HRMS) can be used as an efficient tool for their screening. Simultaneously screening of these samples by hydrophilic interaction liquid chromatography (HILIC) and reversed phase (RP) would help with full identification of suspects and unknown compounds. However, to confirm the identity of the most relevant suspect or unknown compounds, their chemical properties such as retention time behavior, MSn fragmentation and ionization modes should be investigated. Chapter 3 of this thesis discusses the development of a comprehensive workflow to study the retention time behavior of large groups of compounds belonging to emerging contaminants. A dataset consisted of more than 2500 compounds was used for RP/HILIC-LC-HRMS, and their retention times were derived in both Electrospray Ionization mode (+/-ESI). These in silico approaches were then applied on the identification of 10 new transformation products of tramadol, furosemide and niflumic acid (under ozonation treatment). Chapter 4 discusses about the development of a first retention time index system for LC-HRMS. Some practical applications of this RTI system in suspect and non-target screening in collaborative trials have been presented as well. Chapter 5 describes the development of in silico based toxicity models to estimate the acute toxicity of emerging pollutants in the aquatic environment. This would help link the suspect/non-target screening results to the tentative environmental risk by predicting the toxicity of newly tentatively identified compounds. Chapter 6 introduces an automatic and systematic way to perform suspect and non-target screening in LC-HRMS data. This would save time and the data analysis loads and enable the routine application of non-target screening for regulatory or monitoring purpose