Bypassing the Identification: MS2Quant for Concentration Estimations of Chemicals Detected with Nontarget LC-HRMS from MS2 Data.

Nontarget analysis by liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is now widely used to detect pollutants in the environment. Shifting away from targeted methods has led to detection of previously unseen chemicals, and assessing the risk posed by these newly detected chemicals is an important challenge. Assessing exposure and toxicity of chemicals detected with nontarget HRMS is highly dependent on the knowledge of the structure of the chemical. However, the majority of features detected in nontarget screening remain unidentified and therefore the risk assessment with conventional tools is hampered. Here, we developed MS2Quant, a machine learning model that enables prediction of concentration from fragmentation (MS2) spectra of detected, but unidentified chemicals. MS2Quant is an xgbTree algorithm-based regression model developed using ionization efficiency data for 1191 unique chemicals that spans 8 orders of magnitude. The ionization efficiency values are predicted from structural fingerprints that can be computed from the SMILES notation of the identified chemicals or from MS2 spectra of unidentified chemicals using SIRIUS+CSI:FingerID software. The root mean square errors of the training and test sets were 0.55 (3.5×) and 0.80 (6.3×) log-units, respectively. In comparison, ionization efficiency prediction approaches that depend on assigning an unequivocal structure typically yield errors from 2× to 6×. The MS2Quant quantification model was validated on a set of 39 environmental pollutants and resulted in a mean prediction error of 7.4×, a geometric mean of 4.5×, and a median of 4.0×. For comparison, a model based on PaDEL descriptors that depends on unequivocal structural assignment was developed using the same dataset. The latter approach yielded a comparable mean prediction error of 9.5×, a geometric mean of 5.6×, and a median of 5.2× on the validation set chemicals when the top structural assignment was used as input. This confirms that MS2Quant enables to extract exposure information for unidentified chemicals which, although detected, have thus far been disregarded due to lack of accurate tools for quantification. The MS2Quant model is available as an R-package in GitHub for improving discovery and monitoring of potentially hazardous environmental pollutants with nontarget screening.

MS2Tox Machine Learning Tool for Predicting the Ecotoxicity of Unidentified Chemicals in Water by Nontarget LC-HRMS.

To achieve water quality objectives of the zero pollution action plan in Europe, rapid methods are needed to identify the presence of toxic substances in complex water samples. However, only a small fraction of chemicals detected with nontarget high-resolution mass spectrometry can be identified, and fewer have ecotoxicological data available. We hypothesized that ecotoxicological data could be predicted for unknown molecular features in data-rich high-resolution mass spectrometry (HRMS) spectra, thereby circumventing time-consuming steps of molecular identification and rapidly flagging molecules of potentially high toxicity in complex samples. Here, we present MS2Tox, a machine learning method, to predict the toxicity of unidentified chemicals based on high-resolution accurate mass tandem mass spectra (MS2). The MS2Tox model for fish toxicity was trained and tested on 647 lethal concentration (LC50) values from the CompTox database and validated for 219 chemicals and 420 MS2 spectra from MassBank. The root mean square error (RMSE) of MS2Tox predictions was below 0.89 log-mM, while the experimental repeatability of LC50 values in CompTox was 0.44 log-mM. MS2Tox allowed accurate prediction of fish LC50 values for 22 chemicals detected in water samples, and empirical evidence suggested the right directionality for another 68 chemicals. Moreover, by incorporating structural information, e.g., the presence of carbonyl-benzene, amide moieties, or hydroxyl groups, MS2Tox outperforms baseline models that use only the exact mass or log KOW.

ATR-FT-IR spectral collection of conservation materials in the extended region of 4000-80 cm⁻¹.

In this paper, a spectral collection of over 150 ATR-FT-IR spectra of materials related to cultural heritage and conservation science has been presented that have been measured in the extended region of 4000-80 cm(-1) (mid-IR and far-IR region). The applicability of the spectra and, in particular, the extended spectral range, for investigation of art-related materials is demonstrated on a case study. This collection of ATRFT-IR reference spectra is freely available online (http://tera.chem.ut.ee/IR_spectra/) and is meant to be a useful tool for researchers in the field of conservation and materials science.

Non-invasive analysis of natural textile dyes using fluorescence excitation-emission matrices.

Multidimensional fluorescence spectroscopy was assessed as a non-invasive and non-destructive method for the analysis of components in natural textile dyes. Results demonstrate that components in the natural dyes fluoresce and wool's intrinsic fluorescence is, in many cases, not a considerable analytical interferent. In the case of some self-dyed reference yarns, like those dyed with northern and lady's bedstraws, wood horsetail, safflower, salted shield lichen, dyer's madder and cochineal, the fluorescence excitation-emission matrices (EEMs) are sufficiently characteristic for using them as a primary means of identification (or assignment to a family of dyes). With most of the studied yellow and green dyes (heather, silver birch, some bloodred webcap treatments, alkanet), however, the spectra can be used as additional information for identification. This study reports multidimensional fluorescence data for a collection of wools dyed with natural dyes (31 dyed wool yarn samples that were self-dyed with 18 different natural dyes) that were used as references in a case study of two historical textiles for which liquid chromatography-mass spectrometry was used as a confirmatory technique. Given its utility as a rapid and non-destructive/non-invasive method with information-rich multidimensional EEM output, the front-face fluorescence spectroscopy of surfaces using a fiber optic probe is a promising technique for the analysis of dyes on cultural heritage textiles.

Experimental and Computational Study of Aminoacridines as MALDI(-)-MS Matrix Materials for the Analysis of Complex Samples.

Monoaminoacridines (1-, 2-, 3-, 4-, and 9-aminoacridine) were studied for suitability as matrices in the negative ion mode matrix-assisted laser desorption/ionization mass spectrometry (MALDI(-)-MS) analysis of various samples. This is the first study to examine 1-, 2-, and 4-aminoacridine as potential matrix material candidates for MALDI(-)-MS. In addition, spectral (UV-Vis absorption and fluorescence), proton transfer-related (basicity and autoprotolysis), and crystallization properties of these compounds were characterized experimentally and/or computationally. For testing the capabilities of these aminoacridines as matrix materials, four samples related to cultural heritage materials-stearic acid, colophony resin, dyer's madder dye, and a resinous case-study sample from a shipwreck-were analyzed with MALDI(-)-MS. A novel algorithm (implemented as an executable Python script) for MS data analysis was developed to compare the five matrix materials and to help mass spectrometrists rapidly identify peaks originating from the sample and matrix material. It was determined that all five of the studied aminoacridines can successfully be used as matrix materials in MALDI(-)-MS analysis. As an interesting finding, in several cases, the best mass spectra were obtained by using a relatively small amount of matrix material mixed with an excess amount of sample. 3- and 4-aminoacridine outperformed the other aminoacridines in the ease of obtaining acceptable spectra, average number of ions identified in the mass spectra, and low dependence of the sample-to-matrix mass ratio on experimental results.

Multidisciplinary investigation of two Egyptian child mummies curated at the University of Tartu Art Museum, Estonia (Late/Graeco-Roman Periods).

Two ancient Egyptian child mummies at the University of Tartu Art Museum (Estonia) were, according to museum records, brought to Estonia by the young Baltic-German scholar Otto Friedrich von Richter, who had travelled in Egypt during the early 19th century. Although some studies of the mummies were conducted, a thorough investigation has never been made. Thus, an interdisciplinary team of experts studied the remains using the most recent analytical methods in order to provide an exhaustive analysis of the remains. The bodies were submitted for osteological and archaeothanatological study, radiological investigation, AMS radiocarbon dating, chemical and textile analyses, 3D modelling, entomological as well as aDNA investigation. Here we synthesize the results of one of the most extensive multidisciplinary analyses of ancient Egyptian child mummies, adding significantly to our knowledge of such examples of ancient funerary practices.

Identification and classification of textile fibres using ATR-FT-IR spectroscopy with chemometric methods.

The possibility of classification of single- and two-component textile materials using ATR-FT-IR spectra and chemometric methods, principal component analysis (PCA) and discriminant analysis, was assessed. Altogether 89 textile samples belonging to 26 different types (11 one- and 15 two-component textiles) were investigated. It was found that PCA classification using only two or three principal components (PCs) enables identifying different one- and two-component textiles, although with two important limitations: it was not always possible to distinguish between the cellulose-based fibres (cotton, linen and in some cases viscose) and it was only partly possible to distinguish between silk and wool. The statistical discriminant analysis can use as many PCs as there are sample classes and due to that can discriminate between single-component fibres, including viscose from linen and cotton as well as silk from wool. Besides that, in both of these cases, involving optical microscopy as an additional technique enabled unequivocal identification of the fibres. The possibilities of semi-quantitative analysis of mixed fibres (cotton-polyester, wool-polyester and wool-polyamide) with PCA were investigated and it was found that approximate quantitative composition is obtainable if for the mixed fibre sample a number of spectra are averaged in order to minimize the effect of structural inhomogeneity. For approximate content determination 25 spectra of selected two-component samples were registered for calibration and the averaged spectrum for each sample was computed. Due to the structural inhomogeneity of mixed textiles, obtaining accurate quantitative composition from real samples is not possible with ATR-FT-IR. The main problems with ATR-FT-IR-PCA classification are (1) difficulties in getting high quality spectra from some textiles (e.g. polyacrylic), (2) inhomogeneity of the textile fibres in the case of two-component fibres and (3) intrinsic similarity between the spectra of some fibres (e.g. cotton and linen). In order to test the homogeneity of mixed fibres, microscopic and IR-microspectroscopic analysis was carried out.