Biological systems inspire the design of multifunctional materials and devices. However, current syynthetic replicas rarelyy capture the range of structural complexityy observed in natural materials. Prior to the definition of a biomimetic design, a dual investigation with a common set of criteria for comparing the biological material and the replica is required. Here, we deal with this issue by addressing the non-trivial case of insect cuticles tessellated with polygonal microcells with iridescent colors due to the twisted cholesteric organization of chitin fibers. By using hyperspectral imaging within a common methodology, we compare, at several length scales, the textural, structural and spectral properties of the microcells found in the two-band cuticle of the scarab beetle Chrysina gloriosa with those of the polygonal texture formed in flat films of cholesteric liquid crystal oligomers. The hyperspectral imaging technique offers a unique opportunity to reveal the common features and differences in the spectral-spatial signatures of biological and synthetic samples at a 6-nm spectral resolution over 400 nm-1000 nm and a spatial resolution of 150 nm. The biomimetic design of chiral tessellations is relevant to the field of non-specular properties such as deflection and lensing in geometric phase planar optics.

Scarab beetle. A male specimen of _{C. gloriosa} was collected in Saltillo (Coahuila, Mexico) in July 2009.

Oligomers. CLC oligomers from Wacker Chemie GmbH were used.

Optical setup. In the current experiment, we made use of a formerly described reflective hyperspectral microscope, relying on nematic liquid-crystal-based Fourier spectrometry [A. Jullien et al., OPTICA 2017, 4, 400] The lamp source was a tungsten halogen lamp with a stable spectrum. The spatial resolution was fixed by the microscope and was 150 nm. The field of view was 16 µm by 64 µm.

Analysis. The principle of the Fourier algorithm is described in [A. Jullien et al., OPTICA 2017, 4, 400]. However, we proposed to implement independent component analysis (ICA) to decrease the noise level and significantly reduce the analysis time.