Fourier transform infrared spectroscopy and chemometrics for the discrimination of animal fur types

On-site rapid detection of ancient leather using a dual recognition strategy

Leather has been widely used since ancient times, and the discovery of ancient leather is of great value for studying the origin and development of costume culture. However, due to contamination and degradation of leather relics in the buried environment, traditional analytical methods face challenges in detecting microtraces of ancient leather. Therefore, an immunosensor based on a dual recognition strategy was proposed in this work for the detection of leather artifacts at archaeological sites. Anti-collagen antibodies type I (Anti-COL I) and type II (Anti-COL II) were prepared through animal immunization. Next, the antibodies on the surfaces of magnetic beads (MBs) and polystyrene microspheres (PMs) underwent a specific binding reaction with the antigens, which were magnetically separated and placed in sucrose solution, further catalyzed by sucrose invertase on functionalized polystyrene microspheres (FPMs). Finally, the collagen concentration was detected using a personal glucose meter (PGM). The prepared immunosensor exhibited excellent sensitivity, specificity, and stability, with a limit of detection (LOD) of 4.92 ng mL-1, a relative standard deviation (RSD) of 8.39% for sensitivity, and a linear detection range of 10 ng mL-1 to 100 μg mL-1. The coefficient of variation of specificity was less than 4.34%, and the sensor demonstrated a lifespan of up to three weeks. Moreover, the sensor outperforms enzyme-linked immunosorbent assay (ELISA) in terms of accuracy, specificity, and reproducibility. Therefore, this sensor provides a new strategy for the on-site detection of leather artifacts.

Rapid Identification of Ancient Leather Species Using an Enzyme-Linked Immunosorbent Assay Based on Proteomic and Evolutionary Analyses

The species identification of leather artifacts is of great significance for studying the use and spread of ancient leathers; however, the absence of effective detection methods remains an obstacle. Here, we performed a shotgun proteomic analysis to identify the protein composition of ancient leather artifacts. Based on the Swiss-Prot database, 154 proteins were identified. We investigated these proteins using molecular evolution, structural domain, and sequence alignment analyses to select suitable proteins. Two proteins, Kelch-like family member 17 (KLHL17) and Nance-Horan Syndrome actin remodeling regulator (NHS), were selected for antibody preparation. Their binding affinities were determined by antibody potency and surface plasmon resonance (SPR). Furthermore, we developed and optimized an enzyme-linked immunosorbent assay (ELISA) suitable for the species identification of ancient leather artifacts. Two antibodies specifically identified the species of leather samples from goats and cattle, respectively. We established a new method with the advantages of portability, cost-effectiveness, and high sensitivity that was applied to leather species identification. Our study provides an effective detection tool for archeological leather artifacts utilizing the classical proteomics approach and ELISA technique. In addition, this study provides insights into the development of new protein-based methods for the identification of cultural relics.

Discrimination of mongoose hair from domestic cattle hair, human hair, and synthetic fiber using FTIR spectroscopy and chemometric analysis: a rapid, cost-effective, and field-deployable tool for wildlife forensics

Mongoose hair is used to prepare fine brushes, which increases the demand for mongooses to be poached from the wild and brutally bludgeoned to death. Mongooses were listed as Schedule I species under the Indian Wildlife (Protection) Act 1972. Species identification of wildlife case-related samples is necessary to convict a person under this legislation. Microscopy and DNA-based techniques are commonly used to identify mongoose hair in seized brushes. However, in painting brushes, the roots, and the lower part of the hair are mostly trimmed, and only the upper part is used to make the brushes. In addition, brushes are often prepared with mixed hair from mongoose, domestic cattle, human hair, and synthetic fibre. Therefore, the identification of mongoose hair by microscopy and DNA-based techniques is restricted due to the lack of complete strands of hair and the absence of hair roots. Therefore, there is an urgent need to develop an alternative methodology for the identification of mongoose hair from seized articles. FTIR spectroscopy for forensic analysis has gained significant attention over the years because of its sensitivity, specificity, and non-destructive nature. The present study aimed to discriminate Indian grey mongoose (Herpestes edwardsii) hair from domestic cattle hair (domestic water buffalo and domestic cow), human hair, and synthetic fiber based on their chemical composition using FTIR spectroscopy and chemometric analysis. We have taken hair from four individuals for each species, namely Indian grey mongoose, domestic cattle, human hair, and synthetic fibre. The FTIR spectrum was recorded, and partial least-squares discriminant analysis (PLS-DA) was used to discriminate hair and synthetic fiber. The established PLS-DA model showed an R-square value and an RMSE (root mean square error) value of 0.9 and 0.13 respectively. Our preliminary findings have shown that FTIR spectroscopy combined with chemometrics can quickly discriminate Indian grey mongoose hair, domestic cattle hair, human hair, and synthetic fiber, providing crucial evidence for judicial proceedings.

Attenuated Total Reflection Fourier Transform Infrared Spectroscopy and Chemometrics for the Discrimination of Animal Hair Fibers for the Textile Sector

Analyzing the composition of animal hair fibers in textiles is crucial for ensuring the quality of yarns and fabrics made from animal hair. Among others, Fourier transform infrared (FT-IR) spectroscopy is a technique that identifies vibrations associated with chemical bonds, including those found in amino acid groups. Cashmere, mohair, yak, camel, alpaca, vicuña, llama, and sheep hair fibers were analyzed via attenuated total reflection FT-IR (ATR FT-IR) spectroscopy and scanning electron microscopy techniques aiming at the discrimination among them to identify possible commercial frauds. ATR FT-IR, being a novel approach, was coupled with chemometric tools (partial least squares discriminant analysis, PLS-DA), building classification/prediction models, which were cross-validated. PLS-DA models provided an excellent differentiation among animal hair of both camelids and eight animal species. In addition, the combination of ATR FT-IR and PLS-DA was used to discriminate the cashmere hair from different origins (Afghanistan, Australia, China, Iran, and Mongolia). The model showed very good discrimination ability (accuracy 87%), with variance expression of 94.88% and mean squared error of cross-validation of 0.1525.

Sex and blood group determination from hair using ATR-FTIR spectroscopy and chemometrics

Examination of hair with its intact root is commonly used for DNA profiling of the donor. However, its use for gathering other types of information is less explored. Using attenuated total reflectance-Fourier transform infrared spectroscopy, the present study aims to explore other relevant aspects in a non-destructive manner for forensics. Determining the sex and blood group of human hair samples were the major goals of the study. Sex determination was accomplished by analyzing the differential vibrational intensities and stretching of various chemical groups associated with hair and its proteins. Statistical inference of spectral data was performed using chemometric algorithms such as PCA and PLS-DA. The PLS-DA model determined sex with 100% accuracy and blood grouping with an average accuracy of 95%. The present study is the first of its kind to determine sex and blood grouping from human scalp hair shafts, as far as the author knows. By acting as a preliminary screening test, this study could have significant implications for forensic analysis of crime scene samples. Human and synthetic hair were used in validation studies, resulting in 100% accuracy, specificity, and sensitivity, with 0% false positives and false negatives. The technique ATR FTIR spectroscopy could complement the currently used methods of hair analysis such as physical examination and mitochondrial or genomic DNA analysis.

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.

Analysis and comparison of machine learning methods for species identification utilizing ATR-FTIR spectroscopy

Accurate identification of insect species holds paramount significance in diverse fields as it facilitates a comprehensive understanding of their ecological habits, distribution range, and impact on both the environment and humans. While morphological characteristics have traditionally been employed for species identification, the utilization of empty pupariums for this purpose remains relatively limited. In this study, ATR-FTIR was employed to acquire spectral information from empty pupariums of five fly species, subjecting the data to spectral pre-processing to obtain average spectra for preliminary analysis. Subsequently, PCA and OPLS-DA were utilized for clustering and classification. Notably, two wavebands (3000-2800 cm-1 and 1800-1300 cm-1) were found to be significant in distinguishing A. grahami. Further, we established three machine learning models, including SVM, KNN, and RF, to analyze spectra from different waveband groups. The biological fingerprint region (1800-1300 cm-1) demonstrated a substantial advantage in identifying empty puparium species. Remarkably, the SVM model exhibited an impressive accuracy of 100 % in identifying all five fly species. This study represents the first instance of employing infrared spectroscopy and machine learning methods for identifying insect species using empty pupariums, providing a robust research foundation for future investigations in this area.