Rapid non-destructive techniques to identify the traces of Kajal using chemometrics; A comparison of ATR-FTIR and Raman spectroscopy

Enhancing forensic investigations: Identifying bloodstains on various substrates through ATR-FTIR spectroscopy combined with machine learning algorithms

The forensic analysis of bloodstains on various substrates plays a crucial role in criminal investigations. This study presents a novel approach for analyzing bloodstains using Attenuated Total Reflectance Fourier Transform Infrared spectroscopy (ATR-FTIR) in combination with machine learning. ATR-FTIR offers non-destructive and non-invasive advantages, requiring minimal sample preparation. By detecting specific chemical bonds in blood components, it enables the differentiation of various body fluids. However, the subjective interpretation of the spectra poses challenges in distinguishing different fluids. To address this, we employ machine learning techniques. Machine learning is extensively used in chemometrics to analyze chemical data, build models, and extract useful information. This includes both unsupervised learning and supervised learning methods, which provide objective characterization and differentiation. The focus of this study was to identify human and porcine blood on substrates using ATR-FTIR spectroscopy. The substrates included paper, plastic, cloth, and wood. Data preprocessing was performed using Principal Component Analysis (PCA) to reduce dimensionality and analyze latent variables. Subsequently, six machine learning algorithms were used to build classification models and compare their performance. These algorithms comprise Partial Least Squares Discriminant Analysis (PLS-DA), Decision Trees (DT), Logistic Regression (LR), Naive Bayes Classifier (NBC), Support Vector Machine (SVM), and Neural Network (NN). The results indicate that the PCA-NN model provides the optimal solution on most substrates. Although ATR-FTIR spectroscopy combined with machine learning effectively identifies bloodstains on substrates, the performance of different identification models still varies based on the type of substrate. The integration of these disciplines enables researchers to harness the power of data-driven approaches for solving complex forensic problems. The objective differentiation of bloodstains using machine learning holds significant implications for criminal investigations. This technique offers a non-destructive, simple, selective, and rapid approach for forensic analysis, thereby assisting forensic scientists and investigators in determining crucial evidence related to bloodstains.

Rapid and nondestructive analysis of lipstick on different substrates using ATR-FTIR spectroscopy and chemometrics

Lipstick can be an important piece of evidence in crimes like murders, rapes, and suicides. Due to its prevalence, it can be an important corroborative evidence in crime reconstruction. The analysis of such evidence can provide an evidentiary link between the suspect, the victim, object, or the crime scene. We report the use of nondestructive ATR-FTIR spectroscopy combined with chemometrics for the classification of 10 brands of lipsticks with nine samples each. Chemometric method of partial least square-discriminant analysis (PLS-DA) has been employed to interpret the data and classify the samples into their respective classes. The PLS-DA model provides an AUC figure above 0.99 in all brands except one; for which it is slightly less at 0.94. We have also tested the traces of these lipstick samples on different substrates treating them as unknowns in the already trained PLS-DA model. 100% of the samples on nine substrates viz. a cotton, nylon, plastic, dry tissue, denim (blue jeans), wet tissue, nitrile gloves, white paper, and polyester were correctly attributed to their source brand. In conclusion, the results suggest that ATR-FTIR combined with the chemometrics is a rapid, nondestructive, and accurate method for the discrimination and source attribution of lipstick. This study has potential for use in actual forensic casework conditions.

Determination on Tree Species Selection for Lingzhi or Reishi Medicinal Mushroom Ganoderma lucidum (Agaricomycetes) Cultivation by Fourier Transform Infrared and Two-Dimensional Infrared Correlation Spectroscopy

As a wood-degrading Agaricomycetes mushroom, Ganoderma lucidum can be cultivated on broad-leaf hardwoods. Generally, producers care about the yield, but not the quality of G. lucidum cultivated by different tree species. In this study, five broad-leaf hardwood tree species-Quercus variabilis Bl. (Qv), Castanea mollissima Bl. (Cm), Liquidambar formosana Hance (Lf), Dalbergia hupeana Hance (Dh), and Platycarya strobilacea Sieb. et Zucc. (Ps)-were selected for cultivating of G. lucidum. The chemical compositions of G. lucidum fruiting bodies produced by these tree species were determined by Fourier transform infrared and two-dimensional infrared correlation spectroscopy in order to select the most suitable tree species for cultivation. The overall spectra showed less discrimination of each peak variation detected and properly kept most of the primary metabolites. The second derivative unfolded the stagnation of the first spectrum and more base peaks were detected especially in the range of the first two sections. The protein content contained in G. lucidum cultivated on Ps was 92%, like that on Dh. On the other hand, only 27% similarity was determined in G. lucidum cultivated on Ps and Qv. Therefore, the correlation of this range for the protein content can help in tree species selection. The active sequence of 2DIR spectral could be determined by the active bonding of the component reacted to the perturbation. The result could provide a scientific basis for the selection of tree species and the comprehensive utilization of broad-leaf tree resources on G. lucidum cultivation.

Nitrogen-doped carbon nanotubes enhanced Fenton chemistry: Role of near-free iron(III) for sustainable iron(III)/iron(II) cycles

The sluggish kinetics of Fe(II) recovery strongly impedes the scientific progress of Fenton reaction (Fe(II)/H₂O₂) towards practical application. Here, we propose a novel mechanism that metal-free nitrogen-doped carbon nanotubes (NCNT) can enhance Fenton chemistry with H₂O₂ as electron donors by elevating the oxidation potential of Fe(III). NCNT remarkably promotes the circulation of Fe(III)/Fe(II) to produce hydroxyl radical (^•OH) with excellent stability for multiple usages (more than 10 cycles) in the NCNT/Fe(III)/H₂O₂ system. Although carbonyl on NCNT can act as the electron supplier for Fe(III) reduction, the behavior of NCNT is distinct from common reductants such as hydroxylamine and boron. Electrochemical analysis and density functional theory calculation unveil that nitrogen sites of NCNT can weakly bind with Fe(III) to elevate the oxidation potential of Fe(III) (named near-free Fe(III), primarily FeOH²⁺) at pH ranging from 2.0 to 4.0. Without inputs of external stimulations or electron sacrificers, near-free Fe(III) can promote H₂O₂ induced reduction of Fe(III) to initiate Fenton chain reactions for long-lasting generation of ^•OH. To our delight, it is a common property of N-doped carbon materials (e.g., graphene, carbon nanofibers, and acetylene black), our research thus provides a novel, sustainable, and green strategy for promoting Fenton chemistry.