At the level of monomer molecules, the amino acid composition of each hair component has been studied using liquid chromatography and color tests 8, 10, 21, 22. Hair has been studied on different structure levels for many years. Keratin fibers in cuticle are also amorphous and are rich in disulfide linkage, which is responsible for the chemical and mechanical resistance of hair 7, 11, 18, 20. On the other hand, cuticle is the outermost layer and is composed of overlapping flattened cells, like scales, with a total thickness of ~ 5 μm 14, 19. IFAP has amorphous matrix components with a relatively high amount of cysteine and, thus, contributes to the stabilization of the IF structure via disulfide linkage 7, 17, 18. IF is composed of bundles of keratin fibers that form crystalline α-helical coiled-coil structures 15, 16. The macrofibrils have two main compositions: intermediate filaments (IF) and surrounding matrix, called intermediate filament associated proteins (IFAP) 7, 13, 14. Cortex is the dominant inner material of hair, wrapped by cuticle and composed of an assembly of spindle-shaped macrofibrils. Histologically, hair consists of two structures: cortex (85–90%) 9, 10 and cuticle (around 10%) 11, 12. Hair exhibits distinctive properties of high flexibility and high mechanical- and thermal- resistance. A small amount of lipids, water, and pigments are also present in hair 8. Hair is mainly composed of keratin fibers 7. In this study, we investigate the analytical strategies to extract and integrate the information measured by various analytical techniques, applying to sophisticated biological polymers derived from hair. Therein, the association techniques of such manifold data are worth exploring in order to reveal the origins or compositional factors of polymers’ properties. In contrast, experimental investigations of polymers can be conducted by various analytical techniques that target primary, secondary, higher-order, and large-scale structures, respectively. Such complicated polymer structures could not be assessed by previous computational descriptors which only represent “monomer” or “oligomer” structures based on atomic composition, molecular dynamic simulation 1, quantum chemical calculation 2, string notation (e.g., SMILES), and graph representation 3– 6. However, such data integration could be difficult if a subject is complicated if multiple measurements are conducted complementarily and if various complicated information are involved in the measured data.Ī polymer is one of the most challenging analytical subjects since it is composed of a huge number of atoms and the assembled or higher-order structure is also crucial to determine the net properties. Efficient extraction and integration of various types of measured information is an ultimate interest in scientific analysis to comprehensively understand the nature of a subject. These results demonstrated the efficacy of the present strategy to generate and select descriptors from manifold measured data for investigating the nature of sophisticated subjects, such as hair.Ī scientific measurement provides information regarding a subject based on analytical principles. Herein, the stepwise model refinement via selection of adopted descriptors based on importance evaluation identified the most contributive descriptors, which provided an integrative interpretation about the compositional factors, such as α-helix keratins in cortex and bounded water and thermal resistant components in cuticle. The descriptors were associated with the mechanical properties of hair by constructing prediction models using machine learning algorithms. The measured data were processed by different processing techniques, such as spectral differentiation and deconvolution, and then converted into a variety of “measurement descriptors” with different compositional information. Hair samples were analyzed by multiple techniques, including solid-state nuclear magnetic resonance (NMR), time-domain NMR, Fourier transform infrared spectroscopy, and thermogravimetric and differential thermal analysis. In this study, we describe analytical strategies to extract and selectively associate compositional information measured by multiple analytical techniques, aiming to reveal their relationships with physical properties of biopolymers derived from hair. Integrative measurement analysis of complex subjects, such as polymers is a major challenge to obtain comprehensive understanding of the properties.
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