Ce3+ ion doped olgite mineral samples, Na(Sr, Ba)PO4, were prepared by a high temperature solid-state reaction. The sample was investigated through x-ray powder diffraction, FT-IR and FT-Raman spectra measurements. The optical properties under vacuum ultraviolet (VUV) synchrotron radiation and ultraviolet (UV) irradiation are reported for the first time. The investigated samples show a strong absorption in the VUV and UV ranges. The bands corresponding to the 4f1 → 4f05d1 transitions of Ce3+ ions in the host lattices are identified. The barycentre of Ce3+ ions in the host lattices, the host absorption, the crystal field splitting, the emission and the Stokes shifts are presented and discussed. This Ce3+ ion doped material is a potential candidate for plasma display panel (PDP) application.
Mildew of maize seeds may affect their germination rates and reduce crop quality. It is crucial to classify maize seeds efficiently and without destroying their original structure. This study aimed to establish hyperspectral datasets using hyperspectral imaging (HSI) of maize seeds with different degrees of mildew and then classify them using spectral characteristics and machine learning algorithms. Initially, the images were processed with Otus and morphological operations. Each seed’s spectral features were extracted based on its coding, its edge, region of interest (ROI), and original pixel coding. Random forest (RF) models were optimized using the sparrow search algorithm (SSA), which is incapable of escaping the local optimum; hence, it was optimized using a modified reverse sparrow search algorithm (JYSSA) strategy. This reverse strategy selects the top 10% as the elite group, allowing us to escape from local optima while simultaneously expanding the range of the sparrow search algorithm’s optimal solution. Finally, the JYSSA-RF algorithm was applied to the validation set, with 96% classification accuracy, 100% precision, and a 93% recall rate. This study provides novel ideas for future nondestructive detection of seeds and moldy seed selection by combining hyperspectral imaging and JYSSA algorithms based on optimized RF.
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