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

Super-resolution infrared microspectroscopy reveals heterogeneous distribution of photosensitive lipids in human hair medulla

Human hair medulla chemical composition appears mostly homogenous when mapped by FTIR microspectroscopy even when using a synchrotron radiation source (SR-μFTIR) but it is expected to be heterogeneous. We performed sub-micron chemical mapping of hair cortex and medullas using Optical Photothermal Infrared microspectroscopy (OPTIR) and a mid-infrared Quantum Cascade Laser (QCL) source covering the fingerprint and the CH stretching region. Photodamages were observed in the hair cortex at mild laser power and occurred in the hair medulla even at the lowest power settings of the IR QCL pulsed at 100 kHz rate (4 μW/μm² average power density) and visible probe laser (200 μw/μm² average power density). Photoconversion of calcium carboxylates in other molecules, possibly sodium carboxylates, was observed. Attenuation of the IR QCL power by 40% using ZnSe filter and/or high-speed measurements (1000 cm^-1/s) succeeded in almost completely eliminating the photodamages and photoconversion. OPTIR maps and images showed that the medullas were highly heterogeneous at the submicron scale. We found calcium carboxylates, aliphatic lipids and wax esters in small units, hundreds of nanometers in size. The 1470 cm^-1 CO sym stretching peak of calcium carboxylates and the CH₂asym stretching peak from aliphatic lipids proved to be the most efficient peaks to track the distribution of these molecules. OPTIR had enough sensitivity to map accurately only the strongest peaks from lipids and calcium carboxylates, weaker peaks such as the ester CO and sulfoxide SO bands were not accurately detected by OPTIR even when they were shown to be present by SR-μFTIR. Quantification of the medulla components by OPTIR is difficult due to several factors: discontinuous QCL emission, and noise. The weaker peaks such as CH₃, CO, SO are often underestimated or not detected. We demonstrate here that OPTIR can be used to measure, map and image dark, photosensitive samples using very low IR power.

New approach for hair keratin characterization: use of the confocal Raman spectroscopy to assess the effect of a thermal stress on human hair fiber

OBJECTIVE

The objective of our research was to investigate the heat-protecting effect of a product ex vivo and in vivo on human hair fibers.

METHODS

A preparatory study was carried out in order to determine an optimal threshold of thermal stress. For this, the structure of cross-sections of the hair fiber was observed by optical microscopy. Then, Scanning Electron Microscopy (SEM) and Confocal Raman Spectroscopy (CRS) were applied to analyze ex vivo and in vivo morphological and molecular damage in hair structure after heat stress. Finally, in vivo tests were used to collect consumer perception.

RESULTS

The preparatory study enabled us to determine an optimal stress threshold of 10 heating cycle for SEM and 5 heating cycle for CRS. Based on spectral hierarchical classification using Ward's clustering algorithm, the ex vivo Raman results show that the spectral signature of the hair treated and heated is very close to the negative control. This shows that the product preserves the keratin structure after thermal stress. These results were also confirmed by an in vivo Raman analysis performed on hair samples from 5 donors. In concordance with Raman results, SEM show that treated hair present lesser "bubbles" and "crackling" on the hair surface. Finally, the in vivo studies proved that hair was more protected from the heat.

CONCLUSION

The authors concluded that the product shows protective properties with respect to morphological and molecular heat damage. We also demonstrate that the product promotes the α-helix keratin conformation and preserves the S-S disulfide bands.

Quasar: Easy Machine Learning for Biospectroscopy

Data volumes collected in many scientific fields have long exceeded the capacity of human comprehension. This is especially true in biomedical research where multiple replicates and techniques are required to conduct reliable studies. Ever-increasing data rates from new instruments compound our dependence on statistics to make sense of the numbers. The currently available data analysis tools lack user-friendliness, various capabilities or ease of access. Problem-specific software or scripts freely available in supplementary materials or research lab websites are often highly specialized, no longer functional, or simply too hard to use. Commercial software limits access and reproducibility, and is often unable to follow quickly changing, cutting-edge research demands. Finally, as machine learning techniques penetrate data analysis pipelines of the natural sciences, we see the growing demand for user-friendly and flexible tools to fuse machine learning with spectroscopy datasets. In our opinion, open-source software with strong community engagement is the way forward. To counter these problems, we develop Quasar, an open-source and user-friendly software, as a solution to these challenges. Here, we present case studies to highlight some Quasar features analyzing infrared spectroscopy data using various machine learning techniques.