Investigation of Musty Malodor in Consumer Bath Towels with GCxGC and TOFMS

Investigation of Musty Malodor in Consumer Bath Towels — GCxGC-TOFMS Summary

Significance of the topic

Malodors on consumer textiles such as bath towels are a common quality and consumer-satisfaction problem. Odors described as musty can persist after normal laundering and are often caused by trace volatile or semi-volatile compounds, microbial metabolites, or residues from detergents and personal-care products. Analytical characterization of these odor-causing constituents is important for troubleshooting manufacturing, improving laundry and product formulations, guiding mitigation strategies, and supporting consumer-complaint investigations. High-resolution separation combined with sensitive mass spectrometric detection is required because matrix complexity and ultra-trace analyte concentrations frequently mask relevant components in single-dimension analyses.

Objectives and overview of the study

The study aimed to profile volatile and semi-volatile compounds from post-use towel swatches to identify chemical contributors to an observed musty malodor. Four towel swatches were analyzed: three with sensory-detected musty notes and one control with a different dominant odor. The analytical approach combined headspace solid-phase microextraction (HS-SPME) sampling with comprehensive two-dimensional gas chromatography (GCxGC) and time-of-flight mass spectrometry (TOFMS). Both targeted (candidate musty compounds, sulfur compounds, carboxylic acids) and non-targeted (data-driven) workflows were applied to uncover features correlated with malodor.

Methodology

Sample collection and preparation:

• Four post-use towel swatches (approx. 2 x 1.25 in.) — three with musty odor and one control.
• Each swatch was moistened with 300 µL deionized water and transferred to a 20 mL HS-SPME vial.
• Vials were heated at 40 °C for 60 min using an LPAL agitator; headspace sampling occurred between 0.5 and 24 h after heating.
• HS-SPME conditions: 10 min incubation at 65 °C, extraction for 30 min at 65 °C using a tri-phase fiber (PDMS/DVB/C-WR).
• An alkane standard was analyzed in the same method to determine retention indices (RI) for compound confirmation.

Data acquisition and processing:

• Instrument platform: LECO Pegasus BTX 4D GCxGC-TOFMS with ChromaTOF for peak finding, deconvolution, RI calculation, and library searching; ChromaTOF Tile used for automated sample-set differentiation and visualization.
• Data workflows combined automated non-target processing (peak detection, spectral deconvolution, RI matching) with targeted inspections for compounds known or suspected to contribute to musty odor.

Used instrumentation

Key instrumental elements summarized from the experimental setup:

• Headspace sampling: LPAL agitator for controlled heating and equilibration.
• SPME fiber: tri-phase PDMS/DVB/C-WR (chosen for broad volatile/semi-volatile coverage).
• Chromatography: comprehensive two-dimensional GCxGC providing enhanced peak capacity and structured chromatographic space (primary and secondary column pairing).
• Mass spectrometry: Pegasus BTX 4D time-of-flight MS delivering full m/z-range spectra with high sensitivity and fast acquisition, facilitating detection of low-abundance odorants.
• Software: ChromaTOF for deconvolution, library matching, RI calculation; ChromaTOF Tile for automated comparison, heatmap visualization, and highlighting differential features across replicate sample sets.

Main results and discussion

Complexity and analytical advantage:

• Chromatograms showed highly complex volatile profiles where many components overlapped in a single GC dimension; GCxGC provided substantially greater separation power, revealing analytes that were concealed in 1D GC.
• GC deconvolution recovered several coeluting components, but some coelutions exceeded the capacity of deconvolution alone. GCxGC chromatographic separation in the second dimension resolved these cases and improved spectral match scores and RI agreement.

Representative identifications and trends:

• The most intense detected peak across samples was 1-dodecanol, a surfactant/emulsifier commonly used in detergents and personal-care products; its aroma descriptors (waxy, soapy, fatty) make it relevant for overall odor character but it is unlikely the primary cause of musty notes.
• GCxGC separated several coeluting pairs that were ambiguous in 1D GC. Example pairs resolved included eucalyptol with co-eluting propylbenzene, and benzaldehyde with tetralin; in each case GCxGC improved library match scores and RI confirmation.
• Targeted screening identified multiple compounds with literature-reported musty aroma descriptors; these features generally trended higher in the three malodorous towels relative to the control, making them candidate contributors to the observed odor. Specific chemical classes noted include sulfur-containing volatiles and various carboxylic acids, both of which can have strong odor impacts even at low concentrations.
• Non-targeted, comparative analysis with ChromaTOF Tile efficiently highlighted chromatographic regions and features that differentiated the malodorous towels from the control. Heatmap visualization across replicates helped prioritize analytes for further validation.

Benefits and practical applications of the method

• GCxGC coupled with TOFMS substantially increases the number of resolvable features in complex textile headspace samples, improving the likelihood of detecting trace odorants masked in 1D analyses.
• High-resolution spectral data and retention-index confirmation enable more confident tentative identifications, facilitating targeted follow-up or confirmation by standards.
• Combined targeted and non-targeted software workflows accelerate identification of candidate odorants and provide reproducible, sample-set level comparisons valuable for quality assurance, product troubleshooting, and forensic-style investigations of consumer complaints.
• Method is applicable to: product development (reducing odor-active residues), laundry and detergent efficacy studies, microbial source tracing (when combined with microbiology), and routine QC screening for off-odor risk.

Future trends and potential applications

• Integration of GCxGC-TOFMS datasets with machine-learning classifiers to build predictive models for odor occurrence, enabling automated screening of production lots or formulations.
• Improved sample-preparation strategies and fiber chemistries to enhance recovery of highly polar or very volatile odorants and to quantify low-ppb/pptr levels robustly.
• Higher-sensitivity detectors and orbitrap-style or hybrid platforms could complement TOFMS for structural confirmation and exact-mass determinations in challenging cases.
• Coupling chemical profiles with microbiome characterization (e.g., sequencing of towel-associated microbes) to attribute malodors to microbial metabolism versus product residues or environmental contamination.
• Development of standardized, quantitative GCxGC workflows and reference libraries for textile odor investigations to improve cross-lab comparability.

Conclusion

The combined GCxGC-TOFMS approach (Pegasus BTX 4D) with HS-SPME sampling and advanced data processing (ChromaTOF, ChromaTOF Tile) proved effective for characterizing complex volatile profiles from used towel swatches and for highlighting candidate compounds associated with musty malodor. Two-dimensional chromatographic separation revealed analytes obscured in 1D GC, and TOFMS sensitivity enabled detection of low-level features. Automated non-targeted comparisons expedited identification of differential features across samples. The workflow is suitable for product troubleshooting, QA/QC, and research studies aimed at identifying and mitigating textile malodors.

References

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