Three-dimensional (3D) plasmonic structures have been intensively investigated as high performance surface enhanced Raman scattering (SERS) substrates. Here, we demonstrate a 3D biomimetic SERS substrate prepared by deposition of silver nanoparticles (Ag NPs) on the bioscaffold arrays of cicada wings using laser molecular beam epitaxy. This deposition method can offer a large number of nanoparticles with average diameter of ∼10 nm and nanogaps of sub-10 nm on the surface of chitin nanopillars to generate a high density of hotspots. The prepared 3D Ag/cicada SERS substrate shows a limit of detection (LOD) for Rhodamine 6G as low as 10⁻⁷ M, high enhancement factor of 1.09 × 10⁵, and excellent signal uniformity of 6.8%. Moreover, the molecular fingerprints of melamine in infant formula can be directly extracted with an LOD as low as 10 mg L⁻¹, without the need for functional modification. The prepared SERS-active substrate, due to its low cost, high-throughput, and good detection performance, can be widely used in applications such as food safety and environmental monitoring.

Maize is susceptible to mold infection during growth and storage due to its large embryo and high moisture content. Therefore, it is essential to distinguish the moldy sample from healthy groups to prevent the spread of mold and avoid huger economic losses. Catalase is a metabolite in the growth of microorganisms; hence, all maize samples were accurately divided into four moldy grades (health, mild, moderate, and severe levels) by determining their catalase activity. The visible and shortwave near-infrared (Vis-SWNIR) and longwave near-infrared (LWNIR) hyperspectral images were investigated to jointly identify the moldy levels of maize. Spectra and texture information of each maize sample were extracted and used to build the classification models of maize with different moldy levels in pixel-level fusion and feature-level fusion. The result showed that the feature-level fusion of spectral and texture within Vis-SWNIR and LWNIR regions achieved the best results, overall prediction accuracy reached 95.00% for each moldy level, all healthy maize was correctly classified, and none of the moldy samples were misclassified as healthy level. This study illustrated that two hyperspectral image systems, with complementary spectral ranges, combined with feature selection and data fusion strategies, could be used synergistically to improve the classification accuracy of maize with different moldy levels.