Forensic examination of textile fibres using Raman imaging and multivariate analysis

Optimizing Moss and Lichen Transplants as Biomonitors of Airborne Anthropogenic Microfibers

Anthropogenic microfibers (mfs) are synthetic particles composed of cellulose (cotton, rayon, acetate, etc.) or petrochemical-based polymers (i.e., microplastics-MPs) that are less than 5 mm in length. The accumulation of mfs, including MPs, in the moss Hypnum cupressiforme and the lichen Pseudevernia furfuracea was compared in a transplant experiment lasting 6 weeks. We also tested the effects of the bag used for transplants on the accumulation of mfs. Anthropogenic particles trapped by both biomonitors were mostly filamentous (99% mfs), and their number was overall higher in the moss (mean ± s.d. 102 ± 24) than in the lichen (mean ± s.d. 87 ± 17), at parity of sample weight. On average, mfs found in lichen were significantly longer than those found in moss bags, suggesting that lichens are less efficient at retaining smaller mfs. Exposure without the net yielded a higher mfs number accumulation in both species, indicating that "naked" transplants provide greater sensitivity. The calculation of daily fluxes evidenced a loss of mfs in the lichen, suggesting the presence of more stable bonds between moss and mfs. Raman microspectroscopy carried out on about 100 debris confirms the anthropogenic nature of mfs, of which 20% were MPs. Overall results indicate that moss is preferable to lichen in the biomonitoring of airborne mfs especially when exposed naked.

Anti-interference and non-destructive identification of textile fabrics using front-face excitation-emission matrix fluorescence spectroscopy combined with multi-way chemometrics

The identification of trace textile fabrics discovered at crime scenes plays a crucial role in the case of forensic investigations. Additionally, in practical situations, fabrics may be contaminated, making identification more challenging. To address the aforementioned issue and promote the application of fabrics identification in forensic analysis, front-face excitation-emission matrix (FF-EEM) fluorescence spectra coupled with multi-way chemometric methods were proposed for the interference-free and non-destructive identification of textile fabrics. Common commercial dyes in the same color range under different materials (cotton, acrylic, and polyester) that cannot be visually distinguished were investigated, and several binary classification models for the identification of dye were established using partial least squares discriminant analysis (PLS-DA). The identification of dyed fabrics in the presence of fluorescent interference was also taken into consideration. In each kind of pattern recognition model mentioned above, the classification accuracy (ACC) of the prediction set was 100%. The alternating trilinear decomposition (ATLD) algorithm was executed to separate mathematically and remove the interference, and the classification model based on the reconstructed spectra attained an accuracy of 100%. These findings indicate that FF-EEM technology combined with multi-way chemometric methods has broad prospects for forensic trace textile fabric identification, especially in the presence of interference.

Combination of scattering-projection interleaving and random down-sampling for compressive confocal Raman imaging

Parallel excitation with an array of foci is one way to improve the speed of Raman hyperspectral imaging, and random interleaving of its projection has been proved to be a successful strategy for reconstructing the compressed data cube. The so-called SIRI method allows single-acquisition compressive confocal Raman imaging and provides excellent reconstruction fidelity at a high compression ratio. Here, we demonstrate that, when scattering-projection interleaving and randomly down-sampling in the spatial domain are combined, the modified SIRI allows a further reduction in the data acquisition time and an expansion of the imaging region. At a moderate down-sampling rate, the modified SIRI is even superior to its precursor in terms of reconstruction fidelity. A maximum compression ratio of 80 is also reported experimentally with the proposed method.

Forensic Analysis of Textile Synthetic Fibers Using a FT-IR Spectroscopy Approach

Synthetic fibers are one of the most valuable trace lines of evidence that can be found in crime scenes. When textile fibers are analyzed properly, they can help in finding a linkage between suspect, victim, and the scene of the crime. Various analytical techniques are used in the examination of samples to determine relationships between different fabric fragments. In this exploratory study, multivariate statistical methods were investigated in combination with machine learning classification models as a method for classifying 138 synthetic textile fibers using Fourier transform infrared spectroscopy, FT-IR. The data were first subjected to preprocessing techniques including the Savitzky–Golay first derivative method and Standard Normal Variate (SNV) method to smooth the spectra and minimize the scattering effects. Principal Component Analysis (PCA) was built to observe unique patterns and to cluster the samples. The classification model in this study, Soft Independent Modeling by Class Analogy (SIMCA), showed correct classification and separation distances between the analyzed synthetic fiber types. At a significance level of 5%, 97.1% of test samples were correctly classified.