Use of Ion Mobility TWCCSN2 Values in Non-targeted Food Additives Screening

Significance of the Topic

The accurate detection of food additives is critical for regulatory compliance, consumer safety, and exposure assessment. Traditional multi-residue methods often target a limited number of additives or matrices and may generate false positives in complex samples. Integrating ion mobility spectrometry (IMS) with ultra-performance liquid chromatography and high-resolution mass spectrometry (UPLC-IM-MS) introduces collision cross section (TW CCS N2) as an orthogonal identification metric. This enhances specificity, throughput, and confidence in non-targeted food additive screening.

Study Objectives and Overview

This work aimed to develop and evaluate a comprehensive multi-additive screening method for both positive and negative ionizable food additives, including sweeteners, preservatives, antioxidants, and colorings. Key goals included building a TW CCS N2-enabled MS library, assessing the robustness of CCS values as screening parameters, and demonstrating the approach in off-the-shelf food commodities and spiked samples.

Methodology and Instrumentation

The analytical workflow combined UPLC separation with ion mobility and time-of-flight mass spectrometry. Main components included:

• UPLC: ACQUITY UPLC I-Class PLUS system with BEH C18 or HSS T3 column at 45 °C
• IMS-MS: SYNAPT G2-Si operating in ESI+ and ESI– modes, employing HDMSE acquisition
• MS metrics: accurate mass (m/z 50–1200), drift time, and TW CCS N2 values calibrated to IMS/ToF standards
• Software: MassLynx v4.1 for data acquisition, UNIFI v1.94 for CCS library creation and screening, Progenesis QI for data review

Sample preparation used generic dilution for soft drinks and QuEChERS-style extraction for yogurt, followed by dispersive SPE cleanup. Data processing applied retention time, precursor/product ion m/z, and CCS tolerance of ±2% for confident identification.

Main Results and Discussion

The generated library covered key additive classes with measured TW CCS N2 values. In blind testing of various beverages (lemon, strawberry-kiwi, tonic), authorized sweeteners (acesulfame K, sucralose, aspartame), preservatives (citric acid), and natural constituents were consistently identified with mass error <5 ppm and CCS deviation <2%. Matrix-matched spiking experiments confirmed detection of unauthorized additives (glycyrrhizin, alitame) at trace levels. The dual measurement of positive and negative ion CCS values further increased specificity, effectively eliminating false detections in complex matrices.

Benefits and Practical Applications

The combined UPLC-IM-MS approach offers:

• High-throughput, multi-additive screening in a single run
• Enhanced specificity through orthogonal CCS data, reducing false positives
• Robust performance across diverse food matrices
• Capability to detect both authorized and unauthorized additives

Such a workflow aids regulatory monitoring, quality control, and exposure studies by covering a broad range of additives without extensive method redevelopment.

Future Trends and Applications

Advances in IMS technology, such as higher resolution mobility separation and expanded CCS libraries, will further strengthen non-targeted screening. Integration with machine learning for pattern recognition and the extension of CCS libraries to emerging contaminants will optimize food safety surveillance and support rapid decision-making in regulatory and industrial laboratories.

Conclusion

Incorporating TW CCS N2 values into UPLC-IM-MS screening workflows significantly enhances the detection specificity of food additives across complex matrices. The developed library and method demonstrate robust performance, reliable identification of both known and unexpected additives, and streamlined analysis for routine food safety applications.

References

1. Regulation (EC) No 882/2004 and directives 94/35/EC, 94/36/EC, 95/2/EC on food additives screening
2. Goscinny S. et al. Proc. 61st ASMS Conf. 2013
3. McCullagh M. Waters App. Note 720005028EN, 2014
4. Goscinny S. et al. Rapid Commun. Mass Spectrom. 2019
5. McCullagh M. Anal. Chem. 2018