Collision cross section (CCS) databases based on single-laboratory measurements must be cross-validated to extend their use in peak annotation. This work addresses the validation of the first comprehensive TWCCSN2 database for steroids. First, its long-term robustness was evaluated (i.e., a year and a half after database generation; Synapt G2-S instrument; bias within ±1.0% for 157 ions, 95.7% of the total ions). It was further cross-validated by three external laboratories, including two different TWIMS platforms (i.e., Synapt G2-Si and two Vion IMS QToF; bias within the threshold of ±2.0% for 98.8, 79.9, and 94.0% of the total ions detected by each instrument, respectively). Finally, a cross-laboratory TWCCSN2 database was built for 87 steroids (142 ions). The cross-laboratory database consists of average TWCCSN2 values obtained by the four TWIMS instruments in triplicate measurements. In general, lower deviations were observed between TWCCSN2 measurements and reference values when the cross-laboratory database was applied as a reference instead of the single-laboratory database. Relative standard deviations below 1.5% were observed for interlaboratory measurements (<1.0% for 85.2% of ions) and bias between average values and TWCCSN2 measurements was within the range of ±1.5% for 96.8% of all cases. In the context of this interlaboratory study, this threshold was also suitable for TWCCSN2 measurements of steroid metabolites in calf urine. Greater deviations were observed for steroid sulfates in complex urine samples of adult bovines, showing a slight matrix effect. The implementation of a scoring system for the application of the CCS descriptor in peak annotation is also discussed.

Parent and modified mycotoxin analysis remains a challenge because of their chemical diversity, the presence of isomeric forms, and the lack of analytical standards. The creation and application of a collision cross section (CCS) database for mycotoxins may bring new opportunities to overcome these analytical challenges. However, it is still an open question whether common CCS databases can be used independently from the instrument type and ion mobility mass spectrometry (IM-MS) technologies, which utilize different methodologies for determining the gas-phase mobility. Here, we demonstrated the reproducibility of CCS measurements for mycotoxins in an interlaboratory study (average RSD 0.14% ± 0.079) and across different traveling wave IM-MS (TWIMS) systems commercially available (ΔCCS% < 2). The separation in the drift time dimension of critical pairs of isomers for modified mycotoxins was also achieved. In addition, the comparison of measured and predicted CCS values, including regulated and emerging mycotoxins, was addressed.

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.

The synthetic chemicals in food contact materials can migrate into food and endanger human health. In this study, the traveling wave collision cross section in nitrogen values of more than 400 chemicals in food contact materials were experimentally derived by traveling wave ion mobility spectrometry. A support vector machine-based collision cross section (CCS) prediction model was developed based on CCS values of food contact chemicals and a series of molecular descriptors. More than 92% of protonated and 81% of sodiated adducts showed a relative deviation below 5%. Median relative errors for protonated and sodiated molecules were 1.50 and 1.82%, respectively. The model was then applied to the structural annotation of oligomers migrating from polyamide adhesives. The identification confidence of 11 oligomers was improved by the direct comparison of the experimental data with the predicted CCS values. Finally, the challenges and opportunities of current machine-learning models on CCS prediction were also discussed.

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