Forensic Hair Differentiation Using Attenuated Total Reflection Fourier Transform Infrared (ATR FT-IR) Spectroscopy

Species discrimination from hair using ATR-FTIR spectroscopy: Application in wildlife forensics

Hair is a commonly encountered trace evidence in wildlife crimes involving mammals and can be used for species identification which is essential for subsequent judicial proceedings. This proof of concept study aims, to distinguish the black guard hair of three wild cat species belonging to the genus Panthera i.e. Royal Bengal Tiger (Panthera tigris tigris), Indian Leopard (Panthera pardus fusca), and Snow Leopard (Panthera uncia) using a rapid and non-destructive ATR-FTIR spectroscopic technique in combination with chemometrics. A training dataset including 72 black guard hair samples of three species (24 samples from each species) was used to construct chemometric models. A PLS2-DA model successfully classified these three species into distinct classes with R-Square values of 0.9985 (calibration) and 0.8989 (validation). VIP score was also computed, and a new PLS2DA-V model was constructed using variables with a VIP score ≥ 1. External validation was performed using a validation dataset including 18 black guard hair samples (6 samples per species) to validate the constructed PLS2-DA model. It was observed that PLS2-DA model provides greater accuracy and precision compared to the PLS2DA-V model during cross-validation and external validation. The developed PLS2-DA model was also successful in differentiating human and non-human hair with R-Square values of 0.99 and 0.91 for calibration and validation, respectively. Apart from this, a blind test was also carried out using 10 unknown hair samples which were correctly classified into their respective classes providing 100 % accuracy. This study highlights the advantages of ATR-FTIR spectroscopy associated with PLS-DA for differentiation and identification of the Royal Bengal Tiger, Indian Leopard, and Snow Leopard hairs in a rapid, accurate, eco-friendly, and non-destructive way.

A novel approach using ATR-FTIR spectroscopy and chemometric analysis to distinguish male and female human hair samples

This article presents an attempt to discriminate between human male and female hair samples using a single strand of scalp hair. The methodology involves the non-destructive application of ATR-FTIR spectroscopy coupled with chemometric analysis. A total of 96 hair samples, evenly distributed between 48 male and 48 female volunteers from India, were collected. Spectral analysis revealed subtle differences between the two groups, and reliance on visual interpretation might introduce biasness. To avoid subjective biases, chemometric techniques such as principal component analysis (PCA) and partial least square-discriminant analysis (PLS-DA) were employed for enhanced data visualization and separation. PCA results revealed that the first 10 principal components accounted for 93% of the total variance, with three significant PCs. The PLS-DA model demonstrated a remarkable sensitivity and specificity in sex discrimination from hair samples, establishing its efficacy as a robust classification tool. Furthermore, the proposed model exhibited 100% accuracy in predicting unknown samples, underscoring its potential applicability in real-world scenarios. These outcomes affirm the viability of our approach for non-invasive classification of human male and female hair based on single-strand scalp hair analysis.

Attenuated Total Reflection Fourier Transform Infrared Spectroscopy for Forensic Screening of Long-Term Alcohol Consumption from Human Nails

Fourier transform infrared (FT-IR) spectroscopy is used throughout forensic laboratories for many applications. FT-IR spectroscopy can be useful with ATR accessories in forensic analysis for several reasons. It provides excellent data quality combined with high reproducibility, with minimal user-induced variations and no sample preparation. Spectra from heterogeneous biological systems, including the integumentary system, can be associated with hundreds or thousands of biomolecules. The nail matrix of keratin possesses a complicated structure with captured circulating metabolites whose presence may vary in space and time depending on context and history. We developed a new approach by using machine-learning (ML) tools to leverage the potential and enhance the selectivity of the instrument, create classification models, and provide invaluable information saved in human nails with statistical confidence. Here, we report chemometric analysis of ATR FT-IR spectra for the classification and prediction of long-term alcohol consumption from nail clippings in 63 donors. A partial least squares discriminant analysis (PLS-DA) was used to create a classification model that was validated against an independent data set which resulted in 91% correctly classified spectra. However, when considering the prediction results at the donor level, 100% accuracy was achieved, and all donors were correctly classified. To the best of our knowledge, this proof-of-concept study demonstrates for the first time the ability of ATR FT-IR spectroscopy to discriminate donors who do not drink alcohol from those who drink alcohol on a regular basis.

Multidimensional Profiling of Human Body Hairs Using Qualitative and Semi-Quantitative Approaches with SR-XRF, ATR-FTIR, DSC, and SEM-EDX

This study aimed to assess the potential of a multidimensional approach to differentiate body hairs based on their physico-chemical properties and whether body hairs can replace the use of scalp hair in studies linked to forensic and systemic intoxication. This is the first case report controlling for confounding variables to explore the utility of multidimensional profiling of body hair using synchrotron synchrotron microbeam X-ray fluorescence (SR-XRF) for longitudinal and hair morphological region mapping) and benchtop methods, including attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) (complemented with chemometrics analysis), energy dispersive X-ray analysis (EDX) (complemented with heatmap analysis), differential scanning calorimetry (DSC), and scanning electron microscopy (SEM) analysis (complemented by descriptive statistics) to profile different body hairs in terms of their elemental, biochemical, thermal, and cuticle properties. This multidimensional approach provided supportive information to emphasize the intricate and rather complex interplay between the organization and levels of elements and biomolecules within the crystalline and amorphous matrix of different body hairs responsible for the differences in physico-chemical properties between body hairs that are predominantly affected by the growth rate, follicle or apocrine gland activity, and external factors such as cosmetic use and exposure to environmental xenobiotics. The data from this study may have important implications for forensic science, toxicology and systemic intoxication, or other studies involving hair as a research matrix.

Pupal Age Estimation of Sarcophaga peregrina (Diptera: Sarcophagidae) at Different Constant Temperatures Utilizing ATR-FTIR Spectroscopy and Cuticular Hydrocarbons

Sarcophaga peregrina (Robineau-Desvoidy, 1830) (Diptera: Sarcophagidae) is a forensically important flesh fly that has potential value in estimating the PMImin. The precise pupal age estimation has great implications for PMImin estimation. During larval development, the age determination is straightforward by the morphological changes and variation of length and weight, however, the pupal age estimation is more difficult due to anatomical and morphological changes not being visible. Thus, it is necessary to find new techniques and methods that can be implemented by standard experiments for accurate pupal age estimation. In this study, we first investigated the potential of attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy and cuticular hydrocarbons (CHCs) for the age estimations of S. peregrina pupae at different constant temperatures (20 °C, 25 °C, and 30 °C). The orthogonal projections latent structure discrimination analysis (OPLS-DA) classification model was used to distinguish the pupae samples of different developmental ages. Then, a multivariate statistical regression model, partial least squares (PLS), was established with the spectroscopic and hydrocarbon data for pupal age estimations. We identified 37 CHCs with a carbon chain length between 11 and 35 in the pupae of S. peregrina. The results of the OPLS-DA model show a significant separation between different developmental ages of pupae (R2X > 0.928, R2Y > 0.899, Q2 > 0.863). The PLS model had a satisfactory prediction with a good fit between the actual and predicted ages of the pupae (R2 > 0.927, RMSECV < 1.268). The results demonstrate that the variation tendencies of spectroscopy and hydrocarbons were time-dependent, and ATR-FTIR and CHCs may be optimal for the age estimations of pupae of forensically important flies with implications for PMImin estimation in forensic practice.

Attenuated total reflection-Fourier transform infrared spectroscopy: a universal analytical technique with promising applications in forensic analyses

Contemporary criminal investigations are based on the statements made by the victim and the eyewitnesses. They also rely on the physical evidences found in the crime scene. These evidences, and more particularly biological ones, have a great judicial value in the courtroom. They are usually used to revoke the suspect’s allegations and confirm or refute the statements made by the victim and the witnesses. Stains of body fluids are biological evidences highly sought by forensic investigators. In many criminal cases, the success of the investigation relies on the correct identification and classification of these stains. Therefore, the adoption of reliable and accurate forensic analytical methods seems to be of vital importance to attain this objective. Attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR) is a modern and universal analytical technique capable of fingerprint recognition of the analyte using minimal amount of the test sample. The current systematic review aims to through light on the fundamentals of this technique and to illustrate its wide range of applications in forensic investigations. ATR-FTIR is a nondestructive technique which has demonstrated an exceptional efficiency in detecting, identifying and discriminating between stains of various types of body fluids usually encountered in crime scenes. The ATR-FTIR spectral data generated from bloodstains can be used to deduce a wealth of information related to the donor species, age, gender, and race. These data can also be exploited to discriminate between stains of different types of bloods including menstrual and peripheral bloods. In addition, ATR-FTIR has a great utility in the postmortem investigations. More particularly, in estimating the postmortem interval and diagnosing death caused by extreme weather conditions. It is also useful in diagnosing some ambiguous death causes such as fatal anaphylactic shock and diabetic ketoacidosis.

Unlocking the potential of forensic traces: Analytical approaches to generate investigative leads

Forensic investigation involves gathering the information necessary to understand the criminal events as well as linking objects or individuals to an item, location or other individual(s) for investigative purposes. For years techniques such as presumptive chemical tests, DNA profiling or fingermark analysis have been of great value to this process. However, these techniques have their limitations, whether it is a lack of confidence in the results obtained due to cross-reactivity, subjectivity and low sensitivity; or because they are dependent on holding reference samples in a pre-existing database. There is currently a need to devise new ways to gather as much information as possible from a single trace, particularly from biological traces commonly encountered in forensic casework. This review outlines the most recent advancements in the forensic analysis of biological fluids, fingermarks and hair. Special emphasis is placed on analytical methods that can expand the information obtained from the trace beyond what is achieved in the usual practices. Special attention is paid to those methods that accurately determine the nature of the sample, as well as how long it has been at the crime scene, along with individualising information regarding the donor source of the trace.

Rapid and non-destructive differentiation of Shahtoosh from Pashmina/Cashmere wool using ATR FT-IR spectroscopy

Shahtoosh, the most expensive and sought-after wool in the illegal wildlife trade is obtained from the underfur of a critically endangered species-the Tibetan Antelope (Pantholops hodgsonii). It is often adulterated or mixed with the wool of Pashmina goat (Capra aegagrus hircus) for making shawls, scarves and other woolen articles to maximize the profit. The comparable fineness, color and texture, makes it a challenging task in wildlife forensics to differentiate them. In this study, an attempt has been made to differentiate 50 reference unprocessed underfur hairs from five individuals of each species using ATR FT-IR spectroscopy in combination with chemometric tools such as PCA, and PLS-DA. Results of PCA model demonstrated slight overlap and thus failed to distinguish hairs of these two species. Subsequently, PLS-DA model was employed, and also validation tests (external and blind testing) were carried out to ensure the predictive ability of the model, which resulted in 100% accuracy. The results of PLS-DA model exhibited complete differentiation between Shahtoosh, Pashmina and Angora (Oryctolagus cuniculus domesticus) wool used for external validation study with highly significant predictive ability (R-square value 0.99). This proof-of-concept study illustrates the potential of ATR FT-IR spectroscopy to complement current forensic microscopic and DNA based technique to analyze hair evidence in wildlife investigations owing to its rapid and non-destructive nature with high degree of confidence, and its ease-of-use with minimal to no sample preparation.

Sex discrimination from urine traces for forensic purposes using attenuated total reflectance Fourier transform infrared spectroscopy and multivariate data analysis

The characteristics of ATR FT-IR spectroscopy are extremely attractive and escalating popularity in the field of body fluid analysis owing to its non-destructive, rapid, and reliable nature. Herein, the present study establishes that how ATR FT-IR spectroscopy could be utilized as a non-destructive, non-invasive, and confirmatory technique for sex discrimination from dry urine traces. Traces of body fluids are of paramount importance to criminal investigations as a major source of individualization by DNA profiling. However, the significance of DNA profiling from urine traces is highly diminished due to the small amount of DNA in urine traces. For that reason, the sex discrimination between the male and female donors is sorely desirable. In this study, ATR FT-IR spectroscopy in combination with partial least squares-discriminant analysis (PLS-DA) model unequivocally demonstrated the successful sex discrimination of an individual from dry traces of urine with 95.3% accuracy. PCA-Linear Discriminant Analysis (LDA) approach provided 85.2% of accuracy; however, PCA could not provide the sufficient findings for the discrimination of male and female urine spectra. The validation study was conducted and obtained 0% rates of false-positive and negative assignments. Additionally, this study also attended to assess the influence of substrates on the analysis of urine traces and results have been discussed.

A rapid and non-destructive ATR-FTIR spectroscopy method supported by chemometrics for discriminating between facial creams and the classification into herbal and non-herbal brands

Facial creams are considered to be essential beauty items and are used by both females and males on an everyday basis. These can be encountered as an evidentiary material in criminal investigations, particularly in cases related to sexual and physical assaults against women. These are found in trace amounts and therefore their analysis is difficult and also, it must be through non-destructive methods. In the present work ATR-FTIR spectroscopy was employed for the discrimination of 57 samples of face creams out of which 31 were non-herbal and 26 were from herbal category. Visual analysis of the obtained Spectra was done for discrimination purposes but the method was prone to human error and laborious too. The spectroscopic results were analyzed with PCA (Principal Component Analysis) and PLS-DA (Partial least square discriminant analysis) methods. A segregation of samples was seen in the PCA plots to some extent. The class separation and prediction of the samples was performed using PLS-DA method. A good classification was achieved between herbal and non-herbal samples using PLS-DA method. Further, validation of the model was also performed by testing 10 unknown samples.

Colorful Pigments for Hair Dyeing Based on Enzymatic Oxidation of Tyrosine Derivatives

Melanin exists widely in nature and can afford a variety of colors from black to brown and red according to chemical structure differences and specific mixtures. Inspired by nature, this work reports that tyrosine derivatives with different protecting groups at its N- or C-terminal can be enzymatically oxidized into melanin-like pigments with a wide range of colors. The emergence of colorful pigments can be attributed to the incomplete enzymatic oxidation and polymerization caused by the chemical premodification of the tyrosine molecule. The pigments can be deposited on the surface of the hair to obtain a series of colorful and saturated hair dye effects. Moreover, after the pigments were coated on the hair, we can further deposit silver nanoparticles through in situ reduction, making these coatings have anti-inflammatory and antibacterial potential, thereby expanding their potential use for people with low immunity or those who work in hospitals. This work proposes a green and effective way to synthesize colorful pigments with great potential applications in the hair dying and cosmetic industries.

Spectroscopy as a useful tool for the identification of changes with time in post-mortem vitreous humor for forensic toxicology purposes

Vitreous humor (VH) is an alternative biological matrix with a great advantage of longer availability for analysis due to the lack of many enzymes. The use of VH in forensic toxicology may have an added benefit, however, this application requires rapid, simple, non-destructive, and relatively portable analytical analysis methods. These requirements may be met by Fourier transform infrared spectroscopy technique (FT-IR) equipped with attenuated total reflection accessory (ATR). FT-IR spectra of vitreous humor samples, deposited on glass slides, were collected and subsequent chemometric data analysis by means of Hierarchical Cluster Analysis and Principal Component Analysis was conducted. Differences between animal and human VH samples and human VH samples stored for diverse periods of time were detected. A kinetic study of changes in the VH composition up to 2 weeks showed the distinction of FT-IR spectra collected on the 1st and 14th day of storage. In addition, data obtained for the most recent human vitreous humor samples—collected 3 and 2 years before the study, presented successful discrimination of all time points studied. The method introduced was unable to detect mephedrone addition to VH in the concentration of 10 µg/cm3.

Graphic abstract

A new typology of human hair medullas based on lipid composition analysis by synchrotron FTIR microspectroscopy

Human hair is an organ that connects fundamental and applied research with everyday life through the cosmetic industry. Yet, the accurate compositional description of the human hair medulla is lacking due to their small size and difficulty with microextraction. Medullas are thus generally classified based on morphology. We investigated the chemical composition of the human hair medulla using synchrotron based infrared microspectroscopy. We confirmed that lipid signatures dominate the medulla infrared spectrum having 3-20 times higher lipid concentration compared to their surrounding cortex. Human hair medullas contain a mixture of non-esterified and esterified lipids, and carboxylate soaps in various proportions. We reveal the first direct spectroscopic evidence that medulla carboxylates are coordinated to calcium since they exhibit the specific calcium carboxylate signature. Using a representative sample, we observed a strong compositional variability between medullas that was unreported before. We detected calcium carboxylates in 76% of the medullas with one order of magnitude concentration variability between samples. All medullas contained esters with esterification varying by a factor of 30. Moreover, we detected the presence of crystalline calcium stearate in 9% of the medullas. We described a series of spectral markers to characterize medullas based on their lipid composition and propose to classify medullas in four to five groups. Our analysis provides a more detailed understanding of the chemical composition of human hair medullas that may impact cosmetics and biology. The origin and biological meaning of these variations must still be investigated.

Chemometrics in forensic science: approaches and applications

Forensic investigations are often reliant on physical evidence to reconstruct events surrounding a crime. However, there remains a need for more objective approaches to evidential interpretation, along with rigorously validated procedures for handling, storage and analysis. Chemometrics has been recognised as a powerful tool within forensic science for interpretation and optimisation of analytical procedures. However, careful consideration must be given to factors such as sampling, validation and underpinning study design. This tutorial review aims to provide an accessible overview of chemometric methods within the context of forensic science. The review begins with an overview of selected chemometric techniques, followed by a broad review of studies demonstrating the utility of chemometrics across various forensic disciplines. The tutorial review ends with the discussion of the challenges and emerging trends in this rapidly growing field.

Infrared analysis of hair dyeing and bleaching history

Forensic examination of hair is commonly performed to trace its origin and make a connection between a suspect and a crime scene. Such examination is based on subjective microscopic analysis of hair. During the last decade, several spectroscopic approaches have been proposed to make forensic analysis of hair more robust and reliable. Surface-enhanced Raman and attenuated total internal reflection infrared spectroscopies allowed for detection and identification of dyes directly on hair and even differentiation between commercial brands of those colorants. However, these is a question that remains unanswered: can artificial dyes be detected on bleached hair or bleaching can be used to fully erase information about hair coloring? In this study, we report experimental results that provide a clear answer to this question. We show that infrared analysis can be used to differentiate between undyed bleached hair and hair that was colored with both permanent and semi-permanent dyes prior to bleaching. We also show that IR analysis can be used to distinguish between undyed unbleached and undyed bleached hair. We demonstrate that in combination with multivariate statistical analysis, IR analysis can be used to distinguish with 96-100% accuracy between those hair classes.

Spectral deep learning for prediction and prospective validation of functional groups

State-of-the-art identification of the functional groups present in an unknown chemical entity requires the expertise of a skilled spectroscopist to analyse and interpret Fourier transform infra-red (FTIR), mass spectroscopy (MS) and/or nuclear magnetic resonance (NMR) data. This process can be time-consuming and error-prone, especially for complex chemical entities that are poorly characterised in the literature, or inefficient to use with synthetic robots producing molecules at an accelerated rate. Herein, we introduce a fast, multi-label deep neural network for accurately identifying all the functional groups of unknown compounds using a combination of FTIR and MS spectra. We do not use any database, pre-established rules, procedures, or peak-matching methods. Our trained neural network reveals patterns typically used by human chemists to identify standard groups. Finally, we experimentally validated our neural network, trained on single compounds, to predict functional groups in compound mixtures. Our methodology showcases practical utility for future use in autonomous analytical detection.

Postmortem Diagnosis of Fatal Hypothermia by Fourier Transform Infrared Spectroscopic Analysis of Edema Fluid in Formalin-Fixed, Paraffin-Embedded Lung Tissues

The goal of this study was to investigate whether pulmonary edema could become a specific diagnostic marker for fatal hypothermia using Fourier transform infrared (FTIR) spectroscopy in combination with chemometrics. The spectral profile analysis indicated that hypothermia fatalities associated with pulmonary edema fluid contained more β-sheet protein conformational structures than the control causes of death, which included sudden cardiac death, brain injury, cerebrovascular disease, mechanical asphyxiation, intoxication, and drowning. Subsequently, the results of principal component analysis (PCA) further revealed that the content of β-sheet protein conformational structures in the pulmonary edema fluid was the main discriminatory marker between fatal hypothermia and the other causes of death. Ultimately, a robust postmortem diagnostic model for fatal hypothermia using a partial least-squares discriminant analysis (PLS-DA) algorithm was constructed. Pulmonary edema fluid spectra collected from eight new forensic autopsy cases that did not participate in the construction of the diagnostic model were predicted using the model. The results showed the causes of death of all these eight cases were correctly classified. In conclusion, this preliminary study demonstrates that FTIR spectroscopy in combination with chemometrics could be a promising approach for the postmortem diagnosis of fatal hypothermia.

Detection and discrimination of seminal fluid using attenuated total reflectance Fourier transform infrared (ATR FT-IR) spectroscopy combined with chemometrics

Semen is most frequently encountered body fluid in forensic cases apart from blood especially in sexual assault cases. The presence and absence of semen can help in conviction or exoneration of a suspect by either confirming or refuting the claims put forward by the suspect and the victim. However, in the wake of limited studies on non-destructive and rapid analysis of semen, it is fairly difficult. Therefore, it is an increasing demand to pioneer the application of available analytical methods in such manner that non-destructive, automated, rapid, and reliable identification and discrimination of body fluids can be established. In the present study, such a methodological application of attenuated total reflectance Fourier transform infrared (ATR FT-IR) spectroscopy has been put forward as one of the initial steps towards the identification and discrimination/classification of seminal fluid from vaginal fluid and other human biological as well as non-biological look-alike semen substances using chemometric tools which are principal component analysis (PCA), partial least square regression (PLSR), and linear discriminant analysis (LDA). Effect of other simulated factors such as substrate interference, mixing with other body fluids, dilutions, and washing and chemical treatments to the samples has been studied. PCA resulted in 98.8% of accuracy for the discrimination of seminal fluid from vaginal fluid whilst 100% accuracy was obtained using LDA method. One hundred percent discrimination was achieved to discriminate semen from other biological fluids using PLSR and LDA, and from non-biological substances using PCA-LDA models. Furthermore, results of the effect of substrates, chemical treatment, mixing with vaginal secretions, and dilution have also been described.

Forensic discrimination of menstrual blood and peripheral blood using attenuated total reflectance (ATR)-Fourier transform infrared (FT-IR) spectroscopy and chemometrics

Body fluids are one of the most important pieces of evidence encountered in forensic cases especially in cases of sexual assault. Analysis of such evidence can help to establish a link between the perpetrator, the victim, and the crime scene and thereby assist in crime reconstruction. However, one of the biggest challenges faced by the investigators in sexual assault cases is that of ascertaining the issue of consent of the victim. In this matter, differentiation of menstrual blood (either in dried or stained form) from traumatic peripheral blood can give a potential solution on this particular aspect. A number of studies have been attempted to differentiate these two body fluids using various biochemical and serological methods. However, the methods employed are limited by factors such as sample destructivity and non-specificity, and the methods are susceptible to false positive results. In the present study, the scope of attenuated total reflectance (ATR)-Fourier transform infrared (FT-IR) spectroscopy in discriminating samples of menstrual blood and peripheral blood has been investigated, in combination with chemometric tools such as principal component analysis (PCA), partial least square regression (PLSR), and linear discriminant analysis (LDA). PCA resulted in 93.3% accuracy, whereas PLSR and LDA resulted in 100% accuracy for the discrimination of peripheral blood from menstrual blood. Application of PCA for the discrimination of menstrual blood from vaginal fluid and seminal fluid delivered 100% classification. Similarly, 100% classification was achieved while differentiating between menstrual blood and blood look-alike substances. Furthermore, in the current study, the effect of substrates on the analysis of menstrual blood has also been studied and described. Graphical Abstract.

Surface-Enhanced Raman Analysis of Underlaying Colorants on Redyed Hair

Forensic examination of hair evidence can help with establishing a connection between a suspect and a crime scene or demonstrate the absence of such connections. Currently, it is primarily done by a subjective microscopic examination which can only elucidate the species of origin and, if human, the part of the body the hair came from. Several years ago, surface-enhanced Raman spectroscopy (SERS) was proposed for advanced forensic analysis of hair ( Kurouski , D. ; Van Duyne , R. P. In situ detection and identification of hair dyes using surface-enhanced Raman spectroscopy (SERS) . Anal. Chem. 2015 , 87 , 2901 - 2906 . DOI: 10.1021/ac504405u ). It was shown that SERS could be used to determine whether hair was dyed or not and even reveal what commercial hair colorant was used. Expanding upon those findings, we show that SERS is capable of probing the original colorant even if hair was redyed afterward. Specifically, we were able to detect and identify the underlaying blue semipermanent colorant on hair redyed by both black semipermanent and black permanent colorants. We also demonstrate that original black permanent colorant could be detected by SERS if the hair was recolored by blue semipermanent dye. However, it could not if the hair was recolored by another (blue or black) permanent dye. We also provide experimental evidence that SERS can be used to detect the dye on hair colored more than two months prior to its spectroscopic examination. These experimental findings substantially expand capabilities of SERS in forensics.

Conformation changes in human hair keratin observed using confocal Raman spectroscopy after active ingredient application

OBJECTIVE

In hair care cosmetic products' evaluation, one commonly used method is to evaluate the hair appearance as a gold standard in order to determine the effect of an active ingredient on the final state of the hair via visual appreciation. Although other techniques have been proposed for a direct analysis of the hair fibres, they give only surface or structural information, without any accurate molecular information. A different approach based on confocal Raman spectroscopy has been proposed for tracking in situ the molecular change in the keratin directly in the human hair fibres. It presents a high molecular specificity to detect chemical interactions between molecules and can provide molecular information at various depths at the cortex and cuticle levels.

METHODS

To evaluate the potential of confocal Raman spectroscopy in testing the efficiency of cosmetic ingredients on keratin structure, we undertook a pilot study on the effectiveness of a smoothing shampoo on natural human hair, by analysing α-helix and β-sheet spectral markers in the Amide I band and spectral markers specific to the cystin sulfur content.

RESULTS

We confirmed that an active proved to be effective on a gold standard decreases α-helix keratin conformation and promotes β-sheet keratin conformation in the hair fibres. We also showed that treatment with the effective active decreases the intensity of covalent disulfide (S-S at 510 cm-1 ) cross-linking bands of cysteine. These data confirm that the effective active also acts on the tertiary structure of keratin.

CONCLUSION

From these experiments, we concluded that the effective active has a smoothing effect on the human hair fibres by acting on α-helix and β-sheet keratin conformation and on the tertiary structure of keratin. Based on these results, confocal Raman spectroscopy can be considered a powerful technique for investigating the influence of hair cosmetic ingredients on keratin structure in human hair fibres. Moreover, this analytical technique has the advantage of being non-destructive and label free; in addition, it does not require sample extraction or purification and it can be applied routinely in cosmetic laboratories.

Partial least squares-discriminant analysis (PLS-DA) for classification of high-dimensional (HD) data: a review of contemporary practice strategies and knowledge gaps

This review highlights and discusses critically various knowledge gaps in classification modelling using PLS-DA for high dimensional data.

Application of Fourier transform infrared spectroscopy with chemometrics on postmortem interval estimation based on pericardial fluids

Postmortem interval (PMI) evaluation remains a challenge in the forensic community due to the lack of efficient methods. Studies have focused on chemical analysis of biofluids for PMI estimation; however, no reports using spectroscopic methods in pericardial fluid (PF) are available. In this study, Fourier transform infrared (FTIR) spectroscopy with attenuated total reflectance (ATR) accessory was applied to collect comprehensive biochemical information from rabbit PF at different PMIs. The PMI-dependent spectral signature was determined by two-dimensional (2D) correlation analysis. The partial least square (PLS) and nu-support vector machine (nu-SVM) models were then established based on the acquired spectral dataset. Spectral variables associated with amide I, amide II, COO-, C-H bending, and C-O or C-OH vibrations arising from proteins, polypeptides, amino acids and carbohydrates, respectively, were susceptible to PMI in 2D correlation analysis. Moreover, the nu-SVM model appeared to achieve a more satisfactory prediction than the PLS model in calibration; the reliability of both models was determined in an external validation set. The study shows the possibility of application of ATR-FTIR methods in postmortem interval estimation using PF samples.