Forensic Science: FTIR Spectroscopy Applications in Fiber Analysis

This report discusses the use of Fourier Transform Infrared (FTIR) spectroscopy as a versatile and bias-free method for identifying fibers in forensic science. It highlights the importance of reference libraries and datasets in this process, detailing how these resources aid in the identification of single particles and clinical imaging. The report emphasizes the advantages of FTIR, such as its applicability to various sample states and its ability to enhance data quality and reduce study time. It also explores the integration of mass spectrometry with pyrolysis gas chromatography (Py-GC) and thermal extraction desorption gas chromatography (TED-GC) for polymer identification and mass determination. While acknowledging the benefits of automated analysis pipelines and spectral correlation, the report also addresses drawbacks like the need for data optimization and high measurement times. The study concludes by underscoring the significance of statistical analysis and manual clustering in creating adaptable reference databases, which can be expanded for future harmonization of FTIR analysis, ultimately improving the comparability of FTIR studies in forensic science.

Running head: FORENSIC SCIENCE
FORENSIC SCIENCE
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For the purpose of identification of the fibers, the analysis using the Fourier transform
infrared (FTIR) spectroscopy acts as a versatile method since this technique acts as a bias-free
tool for the purpose of the identification, which in turn is quite a challenging task. For this
purpose often a reference library or a reference dataset is required. This library of data is
generally applied to the identification of the single-particles including the process of clinical
imaging that is often based on the FTIR microscopy [1]. This method was advantageous in
nature as this helped to the test the results further and thus help to customize the results along
with the additional entries. The final reference database that was made to optimize using an
intricate helped further to extensively test the fibres on reference datasets along with the
environmental samples [2]. The quality of the data which was ensured by means of correct
particle identification in association to the depiction of the data therefore significantly increased
comparison to that of previous databases. This in turn is able to provide the applicability of the
concept along with highlighting the significance of the work that is presented. Our novel
database provides a reference point for data comparison with future and previous studies on
fibres that are based on different databases [3].
This method if detection is advantageous in case of detection of the mass that is present
within the sample. One of the most important use involves its utility in any form of state like
solutions, liquids, pastes, fibers, powders, surfaces, and gases. It can enhance quality of the data
and reduced the time taken to complete the studies. The signal-to noise ratio has is increased
which has numerous applications in spectroscopy research. Moreover, faster representation of
data along with non-invasiveness have been counted as an advantage. Apart from provision of
newer capabilities pertaining to science, the compactness and informative ways of representation
is one of the advantages. In order to make this process more efficient, the mass spectrometry is

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combined with pyrolysis gas chromatography (Py-GC) or the thermal extraction desorption gas
chromatography (TED-GC). This method is implemented for the identification of the fibres
through the identification of the types of polymers in addition to the determination of the mass of
the fibre that is present in the given sample [4]. Another advantage of using the reference library
is the use of the automated analysis pipeline that was based on the expenditure of time which
helped in the reduction of the large field sizes which could hence be measured. The use of the
spectral correlation along with that of the first derivative related to vector-normalized spectra
used against a reference library for chemical identification.
One of the drawback of using these reference dataset library was that there was a
requirement of optimizing these data numbers of data for the purpose of automated analysis
software which included these defined reference datasets. This was a difficult task to perform
since the design of that database was based on the hierarchical cluster analysis of reference
spectra in the spectral range from 3600 to 1250 cm−1 [2]. Additionally the major drawbacks of
using this process was that this method included the high measurement time which was
necessary for large field sizes. In general the inorganic or the organic matrix of environmental
samples is made to reduce prior to the measurement through the use of the chemical or
enzymatic treatment methods along with the residue concentrated onto filters. Moreover, sample
preparation as well as removal is a difficult process by itself. Spectral resolution during analysis
of the fibers is not high thus absorption bands arising due to different species do not contain any
interferences. It is not able to remove the non-invasive requirements for issues pertaining to
clinical use. For studies involving diseases zonal discrimination as well as orientation are the
possible disadvantages.

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Through the process of statistical analysis along with the manual clustering using the
reference spectra. This is based on the preference of adaptable reference database for the purpose
of the analysis of the fibers which can be provided. During the process of clustering, it is
important to base the knowledge in terms of the general scheme that was able to allow a
straightforward assignment of new materials to existing entries or as entirely new entries [5]. The
database which was generated is used as a benchmark against a number of datasets, which in turn
was used to identify particles of various sizes and materials. Through the use of exemplary test
on an environmental sample, it can be found that the database is applicable to complex sample
material [6]. Moreover, the ADD can be expanded with new spectra in the future, allowing the
harmonization of the FTIR analysis. In addition to this, by providing a reference dataset with
other reference samples along with an environmental sample for the purpose of validation and
comparison in addition to the new and old databases which can be hence be referenced to the
ADD. Thus this significantly increases the comparability of FTIR studies for the purpose of past
and future publications [7].

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References
[1] Baker MJ, Trevisan J, Bassan P, Bhargava R, Butler HJ, Dorling KM, Fielden PR, Fogarty
SW, Fullwood NJ, Heys KA, Hughes C. Using Fourier transform IR spectroscopy to analyze
biological materials. Nature protocols. 2014 Aug;9(8):1771.
[2] Türker-Kaya S, Huck C. A review of mid-infrared and near-infrared imaging: principles,
concepts and applications in plant tissue analysis. Molecules. 2017;22(1):168.
[3] Skoog DA, Holler FJ, Crouch SR. Principles of instrumental analysis. Cengage learning;
2017 Jan 27.
[4] Lenz R, Enders K, Stedmon CA, Mackenzie DM, Nielsen TG. A critical assessment of visual
identification of marine microplastic using Raman spectroscopy for analysis improvement.
Marine pollution bulletin. 2015 Nov 15;100(1):82-91.

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[5] Ishida H, editor. Fourier transform infrared characterization of polymers. Springer Science &
Business Media; 2013 Mar 9.
[6] Baker MJ, Trevisan J, Bassan P, Bhargava R, Butler HJ, Dorling KM, Fielden PR, Fogarty
SW, Fullwood NJ, Heys KA, Hughes C. Using Fourier transform IR spectroscopy to analyze
biological materials. Nature protocols. 2014 Aug;9(8):1771.
[7] Baker MJ, Trevisan J, Bassan P, Bhargava R, Butler HJ, Dorling KM, Fielden PR, Fogarty
SW, Fullwood NJ, Heys KA, Hughes C. Using Fourier transform IR spectroscopy to analyze
biological materials. Nature protocols. 2014 Aug;9(8):1771.