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This study aims the characterization of several tianeptine transformation products in ultrapure water by simulated sunlight irradiation. Tianeptine was completely degraded after 106 h of exposition following pseudo-first-order kinetics (half-life time = 12.0 ± 2.4 h). Furthermore, an ultra-high-performance liquid chromatography coupled with a high-resolution quadrupole time-of-flight-mass spectrometry method was developed and fully validated taking into account different method performance parameters for the quantification of tianeptine in river water up to a concentration of 400 pg L−1. Following a non-targeted approach based on mass data-independent acquisition, eight different transformation products not previously reported in the literature were identified and accordingly elucidated, proposing a photodegradation mechanism based on the accurate tandem mass spectrometry information acquired. Irradiation experiments were replicated for a tianeptine solution prepared in a blank river water sample, resulting in the formation of the same transformation products and similar degradation kinetics. In addition, a toxicity assessment of the photoproducts was performed by in silico method, being generally all TPs of comparable toxicity to the precursor except for TP1, and showing a similar persistence in the environment except for TP2 and TP6, while TP4 was the only TP predicted as mutagenic. The developed method was applied for the analysis of four river water samples.

At present the information regarding the occurrence of human pharmaceuticals (PhaCs) in coral reefs and their potential impacts on the associated fauna is limited. To optimize the collection of data in these delicate environments, we employed a solid-phase microextraction (bioSPME) and liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) procedure that enabled in vivo determinations in soft corals. Specifically, we researched the antibiotics Ofloxacin Sulfamethoxazole and Clarithromycin, the anti-inflammatory Diclofenac Propyphenazone Ketoprofen and Amisulpride, the neuroactive compounds Gabapentin-lactam, the beta-blocker Metoprolol and the antiepileptic Carbamazepine. Reproducibility was between 2.1% and 9.9% and method detection limits LODs) were between 0.2 and 1.6 ng/g and LOQs between 0.8 and 5.4 mg/g. The method was then applied to establish a baseline for the occurrence of these compounds in the Maldivian archipelago. Colonies of Sarcophyton sp. and Sinularia sp. were sampled along an inner-outer reef transect. Five of the ten targeted PhaCs were identified, and 40% of the surveyed coral colonies showed the occurrence of at least one of the selected compounds. The highest concentrations were found inside the atoll rim. Oxoflacin (9.5 ± 3.9 ng/g) and Ketoprofen (4.5 ± 2.3 ng/g) were the compounds with the highest average concentrations. Outside the atoll rim, only one sample showed contamination levels above the detection limit. No significant differences were highlighted among the two surveyed soft coral species, both in terms of average concentrations and bioconcentration factors (BCFs).

Clustering is an unsupervised machine learning methodology widely used in several sciences to find groups of similar patterns in complex data. The results generated by clustering algorithms generally depend on user-defined input parameters such as the number of expected clusters, which can have a great impact on the homogeneity of the identified clusters. Clustering validity indices (CVIs) are an effective method for determining the optimal number of clusters that best fit the natural partition of a dataset. They do not require any underlying assumption nor a priori knowledge about the true dataset structure. Since 1965, many cluster validity indices have been proposed in the literature and used in several different applications. In this paper, the performance of 68 cluster validity indices was evaluated on 21 real-life research and simulated datasets. CVIs were compared on the same partition for each dataset, which was searched for by the k-means clustering algorithm. Multivariate chemometric methods were applied to disclose mutual relationships among the indices and to select those that are more effective in terms of accuracy and reliability.

Approaches of high-level data fusion, also known as consensus, combine predictions of individual models to increase reliability and overcome limitations of single models. Consensus strategies are frequently applied in the framework of Quantitative Structure - Activity Relationships (QSARs) to reduce the uncertainties in the prediction of molecular activities and provide better accuracy of the model outcomes. However, specific regions of the chemical space may systematically be associated with low accuracy and even consensus modelling cannot improve prediction reliability through the multiple outcomes of individual models. In this study, a new heuristic metric to assess the degree of accuracy of consensus predictions in the chemical space is proposed. This metric can assist the mapping of reliability in prediction and enhance the delineation of a safe zone, where consensus predictions are expected to have better accuracy. The new metric is calculated by kernel-based potential functions and it can be used in the framework of both classification and regression consensus modelling. Four case studies, including extensive datasets for consensus modelling, were used to test the proposed approach. Results demonstrated that a potential can be associated with regions of the chemical space as a function of accuracy of consensus modelling and it can be used to enable the mapping of reliability in prediction and the definition of specific regions where predictions are expected to be more reliable.

Natural products are a diverse class of compounds with promising biological properties, such as high potency and excellent selectivity. However, they have different structural motifs than typical drug-like compounds, e.g., a wider range of molecular weight, multiple stereocenters and higher fraction of sp3-hybridized carbons. This makes the encoding of natural products via molecular fingerprints difficult, thus restricting their use in cheminformatics studies. To tackle this issue, we explored over 30 years of research to systematically evaluate which molecular fingerprint provides the best performance on the natural product chemical space. We considered 20 molecular fingerprints from four different sources, which we then benchmarked on over 100,000 unique natural products from the COCONUT (COlleCtion of Open Natural prodUcTs) and CMNPD (Comprehensive Marine Natural Products Database) databases. Our analysis focused on the correlation between different fingerprints and their classification performance on 12 bioactivity prediction datasets. Our results show that different encodings can provide fundamentally different views of the natural product chemical space, leading to substantial differences in pairwise similarity and performance. While Extended Connectivity Fingerprints are the de-facto option to encoding drug-like compounds, other fingerprints resulted to match or outperform them for bioactivity prediction of natural products. These results highlight the need to evaluate multiple fingerprinting algorithms for optimal performance and suggest new areas of research. Finally, we provide an open-source Python package for computing all molecular fingerprints considered in the study, as well as data and scripts necessary to reproduce the results, at https://github.com/dahvida/NP_Fingerprints.

Aroma determination in alcoholic beverages has become a hot research topic due to the ongoing effort to obtain quality products, especially in a globalized market. Consumer satisfaction is mainly achieved by balancing several aroma compounds, which are mixtures of numerous volatile molecules enclosed in challenging matrices. Thus, sample preparation strategies for quality control and product development are required. They involve several steps including copious amounts of hazardous solvents or time-consuming procedures. This is bucking the trend of the ever-increasing pressure to reduce the environmental impact of analytical chemistry processes. Hence, the evolution of sample preparation procedures has directed towards miniaturized techniques to decrease or avoid the use of hazardous solvents and integrating sampling, extraction, and enrichment of the targeted analytes in fewer steps. Mass spectrometry coupled to gas or liquid chromatography is particularly well suited to address the complexity of these matrices. This review surveys advancements of green miniaturized techniques coupled to mass spectrometry applied on all categories of odor-active molecules in the most consumed alcoholic beverages: beer, wine, and spirits. The targeted literature consider progresses over the past 20 years.

Orchids are experiencing wide success in ornamental, medicinal, and food fields. The reason for their success is correlated with both their morphology and metabolomics, the latter linked to their taste and biological effects. Despite many orchids having already been the subject of chemotaxonomic works, some of them are still untapped, like the case of Orchis purpurea. O. purpurea is one of the most common species of the genus Orchis, present in hedgerows, verges, and light woodland, where it is one of the few herbaceous plants able to be unpleasant to herbivorous animals. Essential oil from roots, stems, leaves, and flowers were analyzed via GC/MS analyses, revealing the presence of 70 compounds, with a clear prevalence of coumarin. The high concentration of this metabolite may explain the resistance of O. purpurea to herbivores, being associated with appetite-suppressing properties and a bitter taste. Non-volatile fractions were analyzed via UHPLC-MS analysis revealing the presence of hydroxycinnamic acid derivatives, polyphenols, and glycosidic compounds, probably responsible for their color and fragrance. Taken together, the herein presented results shed light on both the defensive strategy and the chemotaxonomy of O. purpurea.

Biofilm formation is a major cause of hospital-acquired infections. Research into biofilm-resistant materials is therefore critical to reduce the frequency of these events. Polymer microarrays offer a high-throughput approach to enable the efficient discovery of novel biofilm-resistant polymers. Herein, bacterial attachment and surface chemistry are studied for a polymer microarray to improve the understanding of Pseudomonas aeruginosa biofilm formation on a diverse set of polymeric surfaces. The relationships between time-of-flight secondary ion mass spectrometry (ToF-SIMS) data and biofilm formation are analyzed using linear multivariate analysis (partial least squares [PLS] regression) and a nonlinear self-organizing map (SOM). The SOM models revealed several combinations of fragment ions that are positively or negatively associated with bacterial biofilm formation, which are not identified by PLS. With these insights, a second PLS model is calculated, in which interactions between key fragments (identified by the SOM) are explicitly considered. Inclusion of these terms improved the PLS model performance and shows that, without such terms, certain key fragment ions correlated with bacterial attachment may not be identified. The chemical insights provided by the combination of PLS regression and SOM will be useful for the design of materials that support negligible pathogen attachment.

Hyphenated chromatography is among the most popular analytical techniques in omics related research. While great advancements have been achieved on the experimental side, the same is not true for the extraction of the relevant information from chromatographic data. Extensive signal preprocessing is required to remove the signal of the baseline, resolve the time shifts of peaks from sample to sample and to properly estimate the spectra and concentrations of co-eluting compounds. Among several available strategies, curve resolution approaches, such as PARAFAC2, ease the deconvolution and the quantification of chemicals. However, not all resolved profiles are relevant. For example, some take into account the baseline, others the chemical compounds. Thus, it is necessary to distinguish the profiles describing relevant chemistry. With the aim to assist researchers in this selection phase, we have tried three different classification algorithms (convolutional and recurrent neural networks, k-nearest neighbours) for the automatic identification of GC-MS elution profiles resolved by PARAFAC2. To this end, we have manually labelled more than 170,000 elution profiles in the following four classes: ‘Peak’, ‘Cutoff peak’,’ Baseline’ and ‘Others’ in order to train, validate and test the classification models. The results highlight two main points: i) neural networks seem to be the best solution for this specific classification task confirmed by the overall quality of the classification, ii) the quality of the input data is crucial to maximize the modelling performances.

The self-organizing map (SOM) is a nonlinear machine learning algorithm that is particularly well suited for visualizing and analyzing high-dimensional, hyperspectral time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging data. Previously, we compared the capabilities of the SOM with more traditional linear techniques using ToF-SIMS imaging data. Although SOMs perform well with minimal data preprocessing and negligible hyperparameter optimization, it is important to understand how different data preprocessing methods and hyperparameter settings influence the performance of SOMs. While these investigations have been reported outside of the ToF-SIMS field, no such study has been reported for hyperspectral MSI data. To address this, we used two labeled ToF-SIMS imaging datasets, one of which was a polymer microarray dataset, while the other was semisynthetic hyperspectral data. The latter was generated using a novel algorithm that we describe here. A grid-search was used to evaluate which data preprocessing methods and SOM hyperparameters had the largest impact on the performance of the SOM. This was assessed using multiple linear regression, whereby performance metrics were regressed onto each variable defining the preprocessing-hyperparameter space. We found that preprocessing was generally more important than hyperparameter selection. We also found statistically significant interactions between several parameters studied, suggesting a complex interplay between preprocessing and hyperparameter selection. Importantly, we identified interesting trends, both dataset specific and dataset agnostic, which we describe and discuss in detail.

Photoemission electron microscopy (PEEM) is a powerful technique for surface characterization that provides detailed information on the chemical and structural properties of materials at the nanoscale. In this study, the potential is explored using a machine learning algorithm called self-organizing map with a relational perspective map (SOM-RPM) for visualizing and analyzing complex PEEM-generated datasets. The application of SOM-RPM is demonstrated using synchrotron-based X-ray magnetic circular dichroism (XMCD)-PEEM data acquired from a pyrrhotite sample. Traditional visualization approaches for XMCD-PEEM data may not fully capture the complexity of the sample, especially in the case of heterogeneous materials. By applying SOM-RPM to the XMCD-PEEM data, a colored topographic map is created that represents the spectral similarities and dissimilarities among the pixels. This approach allows for a more intuitive and easily interpretable representation of the data without the need of data binning or spectral smoothing. The results of the SOM-RPM analysis are compared to the conventional visualization approach, highlighting the advantages of SOM-RPM in revealing features that are not readily observable in the conventional method. This study suggests that the SOM-RPM approach can be used complimentarily for other PEEM-based measurements, such as core level and valence band X-ray photoelectron spectroscopy.

The capacity to discriminate safe from dangerous compounds has played an important role in the evolution of species, including human beings. Highly evolved senses such as taste receptors allow humans to navigate and survive in the environment through information that arrives to the brain through electrical pulses. Specifically, taste receptors provide multiple bits of information about the substances that are introduced orally. These substances could be pleasant or not according to the taste responses that they trigger. Tastes have been classified into basic (sweet, bitter, umami, sour and salty) or non-basic (astringent, chilling, cooling, heating, pungent), while some compounds are considered as multitastes, taste modifiers or tasteless. Classification-based machine learning approaches are useful tools to develop predictive mathematical relationships in such a way as to predict the taste class of new molecules based on their chemical structure. This work reviews the history of multicriteria quantitative structure-taste relationship modelling, starting from the first ligand-based (LB) classifier proposed in 1980 by Lemont B. Kier and concluding with the most recent studies published in 2022.

With the continuous growth of the real and virtual chemical space, efficient computer-assisted methods are required to discover new substances with desired properties and/or predict properties of interest for untested molecules. These methods rely on the principle that the physicochemical and biological properties of compounds are the effects of their structural characteristics. Therefore, the starting point of any chemo- and bioinformatics application is the conversion of a symbolic representation of the molecular structure into numerical information through the calculation of molecular descriptors. Molecular descriptors encode a wide variety of specific molecular features with a different effect on experimental properties and impact on the perceived chemical similarity between molecules. The choice of molecular descriptors is crucial in determining the chemical space representation and computational modeling outcomes. After introducing the fundamental concepts of molecular descriptors in the current epistemological framework, this chapter reviews some of the well-known classical molecular descriptors and fingerprints.

Multitask learning allows to model multiple tasks simultaneously through information sharing. In the context of quantitative structure activity relationships and computational toxicology, multitask learning is gaining more and more interest, owed to its potential to improve the predictive performance of underrepresented tasks and to predict the multi-property profile of molecules. In this chapter, after introducing the multitask problem formulation, we present a hands-on tutorial on multitask neural networks.

The chemometric philosophy is based on some well-defined and shared principles: multivariate analysis of the problem, searching for the relevant information of data, model validation to produce predictive models, definition of the model applicability domain for reliable predictions and graphical visualization of the relevant information. The verification of food authenticity is fundamentally a classification problem; indeed, samples are labeled according to their authenticity status, that is, authentic or non-authentic. Chemometric methods are usually applied to data that are collected in a numerical table. Chemometric methods usually require a pre-treatment of the raw data in order to be able to extract useful and non-trivial information and discard obvious (i.e., intrinsic in the single variable) and useless information. Soft independent modeling of class analogy is one of the most popular class modeling approaches. Artificial Neural Networks are currently considered among the most important emerging tools in chemometrics.

In this study, we propose a new approach based on Raman hyperspectral imaging and chemometrics for the analysis of chemically heterogeneous surfaces of weathered minerals. A pyrite sample showing a heterogeneous surface with different alteration products, is used to test the validity of the technique. Principal Component Analysis (PCA) was initially used to evaluate data structure and identify Raman phases and vibrational modes related to minor and major weathering features. Afterwards, Multivariate Curve Resolution-alternating least squares (MCR-ALS) was carried out to identify the specific chemical components of the major weathering phases. Finally, for the analysis of the minor weathering phases, K-means clustering was carried out to identify groups of similar pixels of low percentage of variance in the image. Three major components are found in the analyzed sample, corresponding to pyrite and hematite vibrational modes, as well as features of an amorphous alteration product patina. Four additional spectral signatures revealing the presence of sulfates were found after the analysis of minor components. The proposed method enables a semi-quantitative threshold-based characterization of chemical features and provides a visual representation of the phase distribution on the surface of the sample.

According to the 2021 World Drug Report, around 275 million people use drugs of abuse, and 36 million people suffer from addiction, fostering a thriving market for illicit substances. In Italy, 30,083 people were reported to the Judicial Authority for offenses in violation of the Italian Law D.P.R. 309/1990. These offences are sentenced after a qualitative–quantitative analysis of seized materials. Given the large quantity of seized drugs and the need to perform accurate analytical determinations, Italian forensic laboratories struggle to complete analyses in a short time, delaying the entire reporting process needed to achieve sentencing. For this purpose, an UHPLC-MS/MS-based platform was developed at the University of Milano-Bicocca to support law-enforcement authorities. Software was designed to easily manage street seizure acquisition, documentation registration, and sampling. A sensitive UHPLC-MS/MS method was fully validated for the quantification of the traditional illicit substances (cocaine, heroin, 6-MAM, morphine, amphetamine, methamphetamine, MDMA, ketamine, GHB, GBL, LSD, trans-∆9-THC, and THCA) at the ppb level. The final report is relayed to the Prefecture in 3–4 days, even within 24 h for urgent requests. The platform allows for semi-automatic data handling to minimize erroneous results for an accurate report generation by standardized procedures.

Membrane introduction mass spectrometry (MIMS) is a direct mass spectrometry technique used to monitor online chemical systems or quickly quantify trace levels of different groups of compounds in complex matrices without extensive sample preparation steps and chromatographic separation. MIMS utilizes a thin, semi-permeable, and selective membrane that directly connects the sample and the mass spectrometer. The analytes in the sample are pre-concentrated by the membrane depending on their physicochemical properties and directly transferred, using different acceptor phases (gas, liquid or vacuum) to the mass spectrometer. Condensed phase (CP) MIMS use a liquid as a medium, extending the range to new applications to less-volatile compounds that are challenging or unsuitable to gas-phase MIMS. It directly allows the rapid quantification of selected compounds in complex matrices, the online monitoring of chemical reactions (in real-time), as well as in situ measurements. CP-MIMS has expanded beyond the measurement of several organic compounds because of the use of different types of liquid acceptor phases, geometries, dimensions, and mass spectrometers. This review surveys advancements of CP-MIMS and its applications to several molecules and matrices over the past 15 years.

In the original publication [1], there was a mistake in Figure 5 as published. CD3OD was written in place of HDO near the peak at about 4.8 ppm on the 1H NMR spectrum of 4-HBA. The corrected Figure 5 appears below. Figure 6 was published with horizontal orientation instead of vertical one. The graph has also been corrected in the original publication. In Figure S3 of the Supplementary Materials, CD3OD was written in place of HDO near the peak at about 4.8 ppm on the 1H NMR spectrum of catechinic acid (CA). The corrected Figure S3 appears below. There were some language/expression issues in original publication. Excessive information or personal comments not necessary to understand the reported study were included in the original publication. A correction has been made to Abstract (Page 1): The findings seemed not to be reliable because of the existence in the literature of very different findings, because of the high concentration that was attributed to the supposed 4-HBA in the dark mixture, and because of the absence of essential analytical experiments to confirm 4-HBA in AOCA. Particularly, the AOCA chromatograms highlighting a peak attributable to 4-HBA, using commercial 4-HBA as a standard, is missing, as well as investigations concerning the antiviral activity of marketed 4-HBA. A correction has been made to Introduction, Paragraph 4 (Page 2): In a recent study, the main constituent (75%) of AOCA was isolated and named compound 2 [8]. It was identified as the colorless and well-known 4-hydroxy benzoic acid (4-HBA), and it was reported to be responsible for the antiviral activity of AOCA, as 2 had been found to be active on tomato brown rugose fruit virus (ToBRFV). This finding turned out to be very different from what was already widely reported in several specific articles [9–13]. In this regard, the structural identity recently attributed to 2 was validated, since 4-HBA is commonly produced by the thermolytic degradation of some flavonoids [14,15]. However

Antineoplastic drugs are a wide and heterogeneous group of substances that, as universally known, can cause highly severe toxic effects to whoever is exposed. From an occupational safety point of view, surface contaminations inside preparation and administration units are a growing issue and therefore require the development and implementation of sensible and fast monitoring methods. The unlikelihood of a unique all-embracing chromatography, able to correctly retain and separate each analyte led to the need to create an orthogonal normal phase analysis, which might be able to fill the gaps in the more common reversed-phase ones. An existing hydrophilic interaction method has thus been expanded to 6 other drugs and applied to real samples after an evaluation of its performances. The experimental data were then used to evaluate the possibility of estimating reliable relationships between the chromatographic retention and the chemical-structural features of the drugs under analysis.

Mass spectrometry (MS) is widely used for the identification of chemical compounds by matching the experimentally acquired mass spectrum against a database of reference spectra. However, this approach suffers from a limited coverage of the existing databases causing a failure in the identification of a compound not present in the database. Among the computational approaches for mining metabolite structures based on MS data, one option is to predict molecular fingerprints from the mass spectra by means of chemometric strategies and then use them to screen compound libraries. This can be carried out by calibrating multi-task artificial neural networks from large datasets of mass spectra, used as inputs, and molecular fingerprints as outputs. In this study, we prepared a large LC-MS/MS dataset from an on-line open repository. These data were used to train and evaluate deep-learning-based approaches to predict molecular fingerprints and retrieve the structure of unknown compounds from their LC-MS/MS spectra. Effects of data sparseness and the impact of different strategies of data curing and dimensionality reduction on the output accuracy have been evaluated. Moreover, extensive diagnostics have been carried out to evaluate modelling advantages and drawbacks as a function of the explored chemical space.

The dark brown mixture resulting from the autooxidation of catechinic acid (CA) (AOCA) has been reported to possess antiviral activity against Herpes Simplex Virus 1 and 2 (HSV-1 and HSV-2). Unfortunately, the constituents of AOCA were not separated or identified and the compound(s) responsible for AOCA’s antiviral activity remained unknown until recently. Colorless 4-hydroxy benzoic acid (4-HBA) has been reported as the main constituent (75%) of AOCA, and as being responsible for its antiviral activity. The findings seem not to be reliable because of the existence in the literature of very different findings, the high concentration that was attributed to the supposed 4-HBA in the dark mixture, and the insufficient or inaccurately interpreted analytical experiments reported in the study identifying 4-HBA in AOCA. Of particular concern is the lack of AOCA chromatograms highlighting a peak attributable to 4-HBA, using commercial 4-HBA as a standard, and investigations concerning the antiviral activity of marketed 4-HBA. Therefore, in this study, to verify the exactness of the recent reports, we prepared CA from catechin and AOCA from CA, and the absence of 4-HBA in the mixture was first established by thin-layer chromatography (TLC), and then was confirmed by UHPLC–MS/MS, UV–Vis, and ATR–FTIR analyses. For further confirmation, the ATR–FTIR spectral data were processed by principal components analysis (PCA), which unequivocally established strong structural differences between 4-HBA and AOCA. Finally, while the antiviral effects of AOCA against HSV-2 were confirmed, a commercial sample of 4-HBA was completely inactive.

The study concerns the photodegradation of the antidepressant escitalopram (ESC), the S-enantiomer of the citalopram raceme, both in ultrapure and surface water, considering the contribution of indirect photolysis through the presence of nitrate and bicarbonate. The effect of nitrate and bicarbonate concentrations was investigated by full factorial design, and only the nitrate concentration resulted in having a significant effect on the degradation. The kinetics of ESC photodegradation is the pseudo-first-order (half-life = 62.4 h in ultrapure water and 48.4 h in lake water). The generation of transformation products (TPs) was monitored through a developed and validated HPLC-MS/MS method. Fourteen TPs were identified in ultrapure water (one of them, at m/z 261, for the first time) and other two TPs at m/z 327 (found for the first time in this study) were identified only in presence of a nitrate. Several TPs were the same as those formed during the photodegradation of citalopram. The photodegradation pathway of ESC and its mechanism of degradation in water is proposed. The method was applied successfully to the analyses of surface water samples, in which a few dozen of ng L−1 of ESC was determined together with the presence of TP2, TP5 and TP12. Finally, a preliminary in silico evaluation of the toxicological profile and environmental behavior of TPs by computational models was carried out; two TPs (TP4 and TP10) were identified as of potential concern, as they were predicted mutagenic by Ames test model.

A Box-Behnken experimental design was implemented in model wine (MW) to clarify the impact of copper, iron, and oxygen in the photo-degradation of riboflavin (RF) and methionine (Met) by means of response surface methodology (RSM). Analogous experiments were undertaken in MW containing caffeic acid or catechin. The results evidenced the impact of copper, iron, and oxygen in the photo-induced reaction between RF and Met. In particular, considering a number of volatile sulfur compounds (VSCs) that act as markers of light-struck taste (LST), both transition metals can favor VSC formation, which was shown for the first time for iron. Oxygen in combination can also affect the concentration of VSCs, and a lower content of VSCs was revealed in the presence of phenols, especially caffeic acid. The perception of "cabbage"sensory character indicative of LST can be related to the transition metals as well as to the different phenols, with potentially strong prevention by phenolic acids.

I recettori nucleari sono fattori di trascrizione coinvolti in processi critici per la salute umana e sono un obiettivo rilevante per la valutazione del rischio tossicologico e il processo di scoperta dei farmaci. I modelli computazionali possono essere uno strumento utile (i) per dare priorità alla sperimentazione di sostanze chimiche che possono imitare gli ormoni naturali e quindi essere interferenti endocrini e (ii) per identificare nuovi possibili candidati farmaci. Pertanto, l'obiettivo principale di questo progetto è quello di studiare le potenziali interazioni tra sostanze chimiche e recettori nucleari, con il duplice scopo di sviluppare strumenti in silico per la ricerca di nuovi modulatori e di identificare possibili sostanze chimiche che alterano il sistema endocrino. Dopo aver creato una collezione esaustiva di modulatori di recettori nucleari, abbiamo applicato metodi di apprendimento automatico per colmare il vuoto di dati e predire nuovi possibili modulatori tramite modelli predittivi. In particolare, le strategie di modellazione hanno incluso algoritmi di apprendimento automatico multi-tasking per indagare le complesse relazioni tra sostanze chimiche e diversi recettori nucleari., Nuclear receptors are transcription factors involved in processes critical to human health and are a relevant target for toxicological risk assessment and the drug discovery process. Computational models can be a useful tool (i) to prioritize chemicals that can mimic natural hormones and thus be endocrine disruptors and (ii) to identify new possible lead for drug discovery. Therefore, the main goal of this project is to study potential interactions between chemicals and nuclear receptors, with the dual purpose of developing in silico tools to search for new modulators and to identify possible endocrine disrupting chemicals. After creating an exhaustive collection of nuclear receptor modulators, we applied machine learning methods to fill the data gap and prioritize modulators by building predictive models. In particular, modeling strategies included multi-tasking machine learning algorithms to investigate the complex relationships between chemicals and multiple nuclear receptors.

La metabolomica, che consiste nella identificazione di tutti i metaboliti presenti all’interno dei campioni biologici analizzati, è un approccio ampiamente applicato in diversi campi di ricerca quali: identificazione di biomarcatori, sviluppo di nuovi farmaci, scienze alimentari e ambientali. La metabolomica è strettamente legata alla capacità di tecniche analitiche fra queste una delle più applicate è la gas cromatografia accoppiata alla spettrometria di massa. Moderne piattaforme analitiche possono generare centinaia di migliaia di spettri, rilevando una quantità impressionante di molecole distinte. Nonostante i progressi tecnici raggiunti sul lato sperimentale, la conversione dei segnali misurati dagli strumenti in informazioni utili non è un passaggio scontato in studi metabolomici. Per ogni composto identificato, l’obbiettivo è ottenere la concentrazione relativa tra tutti i campioni analizzati e lo spettro di massa associato al composto, necessario per l’identificazione della molecola stessa. I software disponibili per l’analisi dei dati sperimentali sono stati ripetutamente indicati come una fonte importante di incertezza, limitando fortemente sia la quantità che la qualità delle informazioni estratte. Gli strumenti più applicati richiedono l’impostazione di diversi parametri da parte dell’operatore, influenzando il risultato dell’analisi. In questa tesi è descritto un nuovo approccio, chiamato AutoDise, per l’analisi dei dati GC-MS. L’elaborazione dei segnali sperimentali si basa su PARAFAC2. PARAFAC2 è un modello che scompone dati multidimensionali, discriminando tra i diversi segnali nei campioni. Grazie alle sue proprietà, PARAFAC2 non ha bisogno che i dati siano pretrattati e non richiede di impostare parametri, mentre software utilizzati in questo ambito richiedono di definire diversi parametri e un laborioso pretrattamento dei dati, richiedendo l’intervento di un utente esperto, inoltre la riproducibilità dei risultati è limitata, dipen, Metabolomics, which consists of identifying all the metabolites present in the biological samples analysed, is an approach widely applied in various research fields such as biomarker identification, new drug development, food and environmental sciences. Metabolomics is closely linked to the ability of analytical techniques, one of the most widely applied being gas chromatography coupled to mass spectrometry. Modern analytical platforms can generate hundreds of thousands of spectra, detecting an impressive number of distinct molecules. Despite the technical progress achieved on the experimental side, the conversion of signals measured by instruments into useful information is not an obvious step in metabolomic studies. For each identified compound, the goal is to obtain the relative concentration among all analysed samples and the mass spectrum associated with the compound needed to identify the molecule itself. The software available for analysing experimental data has repeatedly been cited as a major source of uncertainty, severely limiting both the quantity and quality of the information extracted. The most applied tools are based on univariate data analysis, considering each sample separately from the others and requiring the operator to set several parameters, affecting the result of the analysis. In this thesis, a new approach, called AutoDise, for the analysis of GC-MS data is described. The processing of the experimental signals is based on PARAFAC2. PARAFAC2 is a model that decomposes multidimensional data, discriminating between different signals in the samples. Due to its properties, PARAFAC2 does not need the data to be pre-processed and does not require parameters to be set, whereas software used in this field requires several parameters to be defined and laborious pre-processing of the data, requiring the intervention of an expert user, and the reproducibility of the results is limited, depending on the parameters chosen by the user. However, fitting PA

Many biologically relevant molecular recognition processes are highly dynamic and rely on the equilibrium between ligands’ association and dissociation from their receptor(s), because of the reversible nature of the majority of them. Over the last few decades, several NMR experiments allowing the characterization of these interactions have been developed. They are very useful tools for the study of naturally occurring binding events, but also for the screening of both natural and synthetic compounds that, through the interaction with relevant targets, can exert biological and pharmacological activities. The aim of this chapter is to provide a general presentation of the most commonly used NMR experiments based on ligand resonance observation, together with the descriptions of specific examples of their advanced applications in the analysis of unconventional samples, such as those containing complex compound mixtures, multivalent ligands, liposomes, nanoparticles and living cells.

Bitter pit (BP) is one of the most relevant post-harvest disorders for apple industry worldwide, which is often related to calcium (Ca) deficiency at the calyx end of the fruit. Its occurrence takes place along with an imbalance with other minerals, such as potassium (K). Although the K/Ca ratio is considered a valuable indicator of BP, a high variability in the levels of these elements occurs within the fruit, between fruits of the same plant, and between plants and orchards. Prediction systems based on the content of elements in fruit have a high variability because they are determined in samples composed of various fruits. With X-ray fluorescence (XRF) spectrometry, it is possible to characterize non-destructively the signal intensity for several mineral elements at a given position in individual fruit and thus, the complete signal of the mineral composition can be used to perform a predictive model to determine the incidence of bitter pit. Therefore, it was hypothesized that using a multivariate modeling approach, other elements beyond the K and Ca could be found that could improve the current clutter prediction capability. Two studies were carried out: on the first one an experiment was conducted to determine the K/Ca and the whole spectrum using XRF of a balanced sample of affected and non-affected ‘Granny Smith’ apples. On the second study apples of three cultivars (‘Granny Smith’, ‘Brookfield’ and ‘Fuji’), were harvested from two commercial orchards to evaluate the use of XRF to predict BP. With data from the first study a multivariate classification system was trained (balanced database of healthy and BP fruit, consisting in 176 from each group) and then the model was applied on the second study to fruit from two orchards with a history of BP. Results show that when dimensionality reduction was performed on the XRF spectra (1.5 - 8 KeV) of ‘Granny Smith’ apples, comparing fruit with and without BP, along with K and Ca, four other elements (i.e., Cl, Si, P

Wine and beer are the most appreciated and consumed beverages in the world. This success is mainly due to their characteristic taste, smell, and aroma, which can delight consumer’s palates. These olfactory characteristics are produced from specific classes of volatile compounds called “volatile odor-active compounds” linked to different factors such as age and production. Given the vast market of drinking beverages, the characterization of these odor compounds is increasingly important. However, the chemical complexity of these beverages has led the scientific community to develop several analytical techniques for extracting and quantifying these molecules. Even though the recent “green-oriented” trend is directed towards direct preparation-free procedures, for some class of analytes a conventional step like derivatization is unavoidable. This review is a snapshot of the most used derivatization strategies developed in the last 15 years for VOAs’ determination in wine and beer, the most consumed fermented beverages worldwide and among the most complex ones. A comprehensive overview is provided for every method, whereas pros and cons are critically analyzed and discussed. Emphasis was given to miniaturized methods which are more consistent with the principles of “green analytical chemistry”.

Heparin has been used successfully as a clinical antithrombotic for almost one century. Its isolation from animal sources (mostly porcine intestinal mucosa) involves multistep purification processes starting from the slaughterhouse (as mucosa) to the pharmaceutical plant (as the API). This complex supply chain increases the risk of contamination and adulteration, mainly with non-porcine ruminant material. The structural similarity of heparins from different origins, the natural variability of the heparin within samples from each source as well as the structural changes induced by manufacturing processes, require increasingly sophisticated methods capable of detecting low levels of contamination. The application of suitable multivariate classification approaches on API 1H NMRspectra serve as rapid and reliable tools for product authentication and the detection of contaminants. Soft Independent Modeling of Class Analogies (SIMCA), Discriminant Analysis (DA), Partial Least Square Discriminant Analysis (PLS-DA) and local classification methods (kNN, BNN and N3) were tested on about one hundred certified heparin samples produced by 14 different manufacturers revealing that Partial Least Squares Discriminant Analysis (PLS-DA) provided the best discrimination of contaminated batches, with a balanced accuracy of 97%.

The purpose of this work is the creation of a chemical database named ChemTastesDB that includes both organic and inorganic tastants. The creation, curation pipeline and the main features of the database are described in detail. The database includes 2944 verified and curated compounds divided into nine classes, which comprise the five basic tastes (sweet, bitter, umami sour and salty) along with four additional categories: tasteless, non-sweet, multitaste and miscellaneous. ChemTastesDB provides the following information for each tastant: name, PubChem CID, CAS registry number, canonical SMILES, class taste and references to the scientific sources from which data were retrieved. The molecular structure in the HyperChem (.hin) format of each chemical is also made available. In addition, molecular fingerprints were used for characterizing and analyzing the chemical space of tastants by means of unsupervised machine learning. ChemTastesDB constitutes a useful tool to the scientific community to expand the information of taste molecules and to assist in silico studies for the taste prediction of unevaluated and as yet unsynthetized compounds, as well as the analysis of the relationships between molecular structure and taste.

The interest in multitask and deep learning strategies has been increasing in the last few years, in application to large and complex dataset for quantitative structure-activity relationship (QSAR) analysis. Multitask approaches allow the simultaneous prediction of molecular properties that are related, through information sharing, whereas deep learning strategies increase the potential of capturing nonlinear relationships. In this work, we compare the binary classification capability of multitask deep and shallow neural networks to single-task strategies used as benchmark (i.e., as k-nearest neighbours, N-nearest neighbours, random forest and Naïve Bayes), as well as multitask supervised self-organizing maps. Comparison was carried out with an extended QSAR dataset containing annotations of molecular binding, agonism and antagonism activity on 11 nuclear receptors, for a total of 14,963 molecules, divided into training and test sets and labelled for their bioactivity on at least one of 30 binary tasks. Additional 304 chemicals were used as external evaluation set to further validate models. Although no approach systematically overperformed the others, task-specific differences were found, suggesting the benefit of multitask learning for tasks that are less represented. On average, some of the single-task approaches and multitask deep learning strategies had similar performances. However, the latter can have advantages, such as a simpler management of predictions and applicability domain assessment for future samples. On the other hand, the parameter tuning required by neural networks are generally time expensive suggesting that the modelling strategy should be evaluated case by case.

Several public efforts are aimed at discovering patterns or classifiers in the high-dimensional bioactivity space that predict tissue, organ or whole animal toxicological endpoints. The current study sought to assess and compare the predictions of the Globally Harmonized System (GHS) categories and Dangerous Goods (DG) classifications based on Lethal Dose (LD50) from several available tools (ACD/Labs, Leadscope, T.E.S.T., CATMoS, CaseUltra). External validation was done using dataset of 375 substances to demonstrate their predictive capacity. All models showed very good performance for identifying non-toxic compounds, which would be useful for DG classification, developing or triaging new chemicals, prioritizing existing chemicals for more detailed and rigorous toxicity assessments, and assessing non-active pharmaceutical intermediates. This would ultimately reduce animal use and improve risk assessments. Category-to-category prediction was not optimal, mainly due to the tendency to overpredict the outcome and the general limitations of acute oral toxicity (AOT) in vivo studies. Overprediction does not specifically pose a risk to human health, it can impact transport and material packaging requirements. Performance for compounds with LD50 ≤ 300 mg/kg (approx. 5% of the dataset) was the poorest among all groups and could be potentially improved by including expert review and read-across to similar substances.