RESUMEN
This work demonstrates high-throughput screening of personal care products to provide an overview of potential exposure. Sixty-seven products from five categories (body/fragrance oil, cleaning product, hair care, hand/body wash, lotion, sunscreen) were rapidly extracted and then analyzed using suspect screening by two-dimensional gas chromatography (GCxGC) high-resolution mass spectrometry (GCxGC-HRT). Initial peak finding and integration were performed using commercial software, followed by batch processing using the machine learning program Highlight. Highlight automatically performs background subtraction, chromatographic alignment, signal quality review, multidilution aggregation, peak grouping, and iterative integration. This data set resulted in 2,195 compound groups and 43,713 individual detections. Compounds of concern (101) were downselected and classified as mild irritants (29%), environmental toxicants/severe irritants (51%) and endocrine disrupting chemicals/carcinogens (20%). High risk compounds such as phthalates, parabens, and avobenzone were detected in 46 out of the 67 products (69%), and only 5 out of the 67 products (7%) listed these compounds on their ingredient labels. The Highlight results for the compounds of concern were compared to commercial software results (ChromaTOF) and 5.3% of the individual detections were discerned only by Highlight, demonstrating the strength of the iterative algorithm to effectively discover low-level signatures. Highlight provides a significant labor advantage, requiring only 2.6% of the time estimated for a largely manual workflow using commercial software. In order to address significant time needed for postprocessing assignment of identification confidence, a new machine-learning-based algorithm was developed to assess the quality of assigned library matches, and a balanced accuracy of 79% was achieved.
Asunto(s)
Cosméticos , Irritantes , Humanos , Programas Informáticos , Algoritmos , Cromatografía de Gases y Espectrometría de Masas/métodosRESUMEN
Masks constructed of a variety of materials are in widespread use due to the COVID-19 pandemic, and people are exposed to chemicals inherent in the masks through inhalation. This work aims to survey commonly available mask materials to provide an overview of potential exposure. A total of 19 mask materials were analyzed using a nontargeted analysis two-dimensional gas chromatography (GCxGC)-mass spectrometric (MS) workflow. Traditionally, there has been a lack of GCxGC-MS automated high-throughput screening methods, resulting in trade-offs with throughput and thoroughness. This work addresses the gap by introducing new machine learning software tools for high-throughput screening (Floodlight) and subsequent pattern analysis (Searchlight). A recursive workflow for chemical prioritization suitable for both manual curation and machine learning is introduced as a means of controlling the level of effort and equalizing sample loading while retaining key chemical signatures. Manual curation and machine learning were comparable with the mask materials clustering into three groups. The majority of the chemical signatures could be characterized by chemical class in seven categories: organophosphorus, long chain amides, polyethylene terephthalate oligomers, n-alkanes, olefins, branched alkanes and long-chain organic acids, alcohols, and aldehydes. The olefin, branched alkane, and organophosphorus components were primary contributors to clustering, with the other chemical classes having a significant degree of heterogeneity within the three clusters. Machine learning provided a means of rapidly extracting the key signatures of interest in agreement with the more traditional time-consuming and tedious manual curation process. Some identified signatures associated with plastics and flame retardants are potential toxins, warranting future study to understand the mask exposure route and potential health effects.