Non-Targeted Screening of Extractables and Leachables in E-Cigarettes Using UPLC and GC Coupled to QTof-MS

Importance of the Topic

Characterization of extractables and leachables in e-cigarette devices is crucial to ensure consumer safety, product quality, and regulatory compliance. Non-targeted screening helps uncover unknown compounds migrating from device materials into the aerosol, providing comprehensive chemical profiles beyond targeted analyses.

Objectives and Study Overview

This application note demonstrates a non-targeted workflow for identifying extractables and leachables in various e-cigarette components. The study aimed to profile chemical constituents from end caps, mouthpiece, gauze with flavor formulation, paper wrap, and metal shell using coupled UPLC and GC with a single QTof mass spectrometer. Data were compared against blanks, and compound identification leveraged accurate mass libraries.

Methodology and Used Instrumentation

The e-cigarette components were individually extracted with isopropanol for 30 minutes. Analyses were performed using:

• UPLC system: ACQUITY UPLC I-Class with BEH C18 column (2.1 × 100 mm, 1.7 µm) at 45 °C, employing a water/methanol gradient with ammonium acetate buffer.
• GC system: Agilent A7890 with APGC interface and DB-5MS column (30 m × 0.25 mm, 0.25 µm), using splitless injection and temperature ramp from 35 °C to 320 °C.
• Mass spectrometer: Xevo G2-XS QTof, alternating low- and high-energy MS E acquisition across 50–1200 m/z, with lockmass calibration (leucine enkephalin for UPLC, siloxane for GC).
• Data processing: UNIFI Scientific Information System for precursor and fragment library matching, adduct and isotopic fitting, and sample-blank comparisons.

Main Results and Discussion

GC-QTof-MS screening identified plasticizers such as dibutyl phthalate (DBP) in internal end cap, metal shell, and gauze, based on high response (>11 000), low mass error (<2.5 ppm), and presence of characteristic fragment ions. UPLC-QTof-MS detected HMBTAD, a light stabilizer, across the same components with strong signal (>42 000) and low mass error (<1.5 ppm), notably elevated in the gauze-flavor extract.
A broader panel of potential extractables—including octadecanoic acid, di-n-octyl sebacate, 4-methyl benzophenone, sorbic acid, N,N-dimethyl-p-phenylenediamine, dyes, antioxidants, and other stabilizers—was tentatively assigned by matching accurate mass data to the UNIFI library.

Benefits and Practical Applications

• Comprehensive non-targeted detection of known and unknown migrants from device materials.
• Streamlined data review through automated library matching and MS E acquisition.
• Adaptable workflow applicable to e-cigarettes, pharmaceutical, food, and cosmetic packaging.

Future Trends and Potential Applications

The integration of high-resolution MS with advanced informatics will drive enhanced sensitivity and confidence in extractable and leachable studies. Anticipated developments include expanded spectral libraries, machine learning for unknown characterization, and standardized protocols supporting global regulatory harmonization. Extended workflows may address real-time monitoring of aerosol emissions and long-term stability assessments.

Conclusion

A unified UPLC-GC-QTof-MS platform coupled with UNIFI informatics enables robust non-targeted screening of e-cigarette extractables and leachables. The approach effectively identifies plasticizers, stabilizers, and other additives across device components, supporting safety assessments and regulatory requirements.

References

• FDA Deeming Regulation (May 2016) – FDA’s New Regulations for E-Cigarettes, Cigars, and All Other Tobacco Products.
• EU Tobacco Products Directive (TPD2; 2014/40/EU).
• Medicines and Healthcare Products Regulatory Agency (MHRA) guidance on e-cigarette regulations (2016).