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Prediction of Collision Cross-Section Values for Extractables and Leachables from Plastic Products.

ABSTRACT: The use of ion mobility separation (IMS) in conjunction with high-resolution mass spectrometry has proved to be a reliable and useful technique for the characterization of small molecules from plastic products. Collision cross-section (CCS) values derived from IMS can be used as a structural descriptor to aid compound identification. One limitation of the application of IMS to the identification of chemicals from plastics is the lack of published empirical CCS values. As such, machine learning techniques can provide an alternative approach by generating predicted CCS values. Herein, experimental CCS values for over a thousand chemicals associated with plastics were collected from the literature and used to develop an accurate CCS prediction model for extractables and leachables from plastic products. The effect of different molecular descriptors and machine learning algorithms on the model performance were assessed. A support vector machine (SVM) model, based on Chemistry Development Kit (CDK) descriptors, provided the most accurate prediction with 93.3% of CCS values for [M + H]⁺ adducts and 95.0% of CCS values for [M + Na]⁺ adducts in testing sets predicted with <5% error. Median relative errors for the CCS values of the [M + H]⁺ and [M + Na]⁺ adducts were 1.42 and 1.76%, respectively. Subsequently, CCS values for the compounds in the Chemicals associated with Plastic Packaging Database and the Food Contact Chemicals Database were predicted using the SVM model developed herein. These values were integrated in our structural elucidation workflow and applied to the identification of plastic-related chemicals in river water. False positives were reduced, and the identification confidence level was improved by the incorporation of predicted CCS values in the suspect screening workflow.

SUBMITTER: Song XC

PROVIDER: S-EPMC9261268 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:In recent years, ion mobility spectrometry-mass spectrometry (IMS-MS) has advanced the field of omics-based research, especially with the development of high-resolution platforms; however, these separations have generally been qualitative in nature. The rotationally averaged ion neutral collision cross section (CCS) is one of the only quantitative metrics available for aiding in characterizing biomolecules in IMS-MS. However, determining the CCS of an ion for multipass IMS systems, such as in cyclic ion mobility-mass spectrometry (cIMS-MS) and structures for lossless ion manipulations, has been challenging due to the lack of methods available for calculating CCS when more than a single pass is required for separation as well as the laborious nature of requiring calibrants and unknown compounds to be subjected to identical number of passes, which may not be possible in certain instances because of peak splitting, high levels of diffusion, etc. Herein, we present a general method that uses average ion velocities for calculating CCS values in cIMS-MS-based separations. Initially, we developed calibration curves using common CCS calibrants [i.e., tetra-alkylammonium salts, polyalanine, and hexakis(fluoroalkoxy)phosphazines] at different traveling wave (TW) conditions and the calculated cIMS CCS values were within ∼1% error or less compared to previously established drift tube IMS CCS measurements. Since it has been established that glycans can split into their α/β anomers, we utilized this method for two glycan species, 2α-mannobiose and melibiose. Both glycans were analyzed at the same TW conditions as the calibrants, and we observed anomer splitting at pathlengths of 20 m for 2α-mannobiose and 40 m for melibiose and thus assigned two unique CCS values for each glycan, which is the first time this has ever been done. We have demonstrated that the use of average ion velocities is a robust approach for obtaining CCS values with good agreement to CCS measurements from the previous literature and anticipate that this methodology can be applied to any IMS-MS platform that utilizes multipass separations. Our future work aims to incorporate this methodology for the development of a high-resolution CCS database to aid in the characterization of human milk oligosaccharides.

Project description:The anthropogenic entry of organic micropollutants into the aquatic environment leads to a potential risk for drinking water resources and the drinking water itself. Therefore, sensitive screening analysis methods are needed to monitor the raw and drinking water quality continuously. Non-target screening analysis has been shown to allow for a more comprehensive investigation of drinking water processes compared to target analysis alone. However, non-target screening is challenging due to the many features that can be detected. Thus, data processing techniques to reduce the high number of features are necessary, and prioritization techniques are important to find the features of interest for identification, as identification of unknown substances is challenging as well. In this study, a drinking water production process, where drinking water is supplied by a water reservoir, was investigated. Since the water reservoir provides surface water, which is anthropogenically influenced by wastewater treatment plant (WWTP) effluents, substances originating from WWTP effluents and reaching the drinking water were investigated, because this indicates that they cannot be removed by the drinking water production process. For this purpose, ultra-performance liquid chromatography coupled with an ion-mobility high-resolution mass spectrometer (UPLC-IM-HRMS) was used in a combined approach including target, suspect and non-target screening analysis to identify known and unknown substances. Additionally, the role of ion-mobility-derived collision cross sections (CCS) in identification is discussed. To that end, six samples (two WWTP effluent samples, a surface water sample that received the effluents, a raw water sample from a downstream water reservoir, a process sample and the drinking water) were analyzed. Positive findings for a total of 60 substances in at least one sample were obtained through quantitative screening. Sixty-five percent (15 out of 23) of the identified substances in the drinking water sample were pharmaceuticals and transformation products of pharmaceuticals. Using suspect screening, further 33 substances were tentatively identified in one or more samples, where for 19 of these substances, CCS values could be compared with CCS values from the literature, which supported the tentative identification. Eight substances were identified by reference standards. In the non-target screening, a total of ten features detected in all six samples were prioritized, whereby metoprolol acid/atenolol acid (a transformation product of the two β-blockers metoprolol and atenolol) and 1,3-benzothiazol-2-sulfonic acid (a transformation product of the vulcanization accelerator 2-mercaptobenzothiazole) were identified with reference standards. Overall, this study demonstrates the added value of a comprehensive water monitoring approach based on UPLC-IM-HRMS analysis.

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Prediction of Collision Cross-Section Values for Extractables and Leachables from Plastic Products.

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