The thermal desorption/pyrolysis-direct analysis in real
time-mass
spectrometry (TD/Py-DART-MS) method was developed for the analysis
of fibers in this study. The fiber samples were pyrolyzed with a temperature
gradient and the pyrolysis products were determined by DART-MS. The
pyrogram from the TD/Py-DART-MS fiber analysis was found to be associated
with the physical properties such as the melting points. At the same
time, the TD/Py-DART-MS allows the analyst to obtain the chemical
information such as polymeric backbone structures and dyes on the
fiber. The pyrolysis profiles of common polymeric fibers in textile
materials such as cotton, cellulose triacetate (CT), poly(caprolactam)
(nylon-6), poly(hexamethylene adipamide) (nylon-6/6), poly(acrylonitrile)
(PAN), poly(ethylene terephthalate) (PET), poly(butylene terephthalate)
(PBT), poly(propylene) (PP), and polytrimethylene terephthalate (PTT)
and their respective characteristic mass spectra were reported in
this study. The fibers from 40 commercial textile samples were analyzed
by the TD/Py-DART-MS method, and the statistical methods including
principal component analysis (PCA) and Pearson product moment correlation
(PPMC) were applied to classify and associate the fibers based on
their mass spectral data. The strong correlation between the reference
fiber mass spectral profiles and tested fiber mass spectral profiles
was observed by using the PPMC method, and the identification accuracy
was 97.5%. When combined, the mass spectral and pyrogram data, the
types of fibers including the blended fibers were identified effectively.
The TD/Py-DART-MS method also demonstrated the promising capability
for the identification of dyes on fibers. Overall, the TD/Py-DART-MS
method requires small sample size and minimal sample preparation but
offers reproducible and multidimensional information for the fiber
evidence rapidly (i.e., ∼6.7 min). Since the proposed method
is simple to perform and the data are easy to interpret, this approach
may significantly contribute to the fiber identification and comparison
procedures in forensic settings with high sample throughput potential.