Fundamental research on selective arsenic removal from high-salinity alkaline wastewater.

The alkaline leaching process of arsenic-containing solid waste discharged during nonferrous metal smelting affords typical high-salinity alkaline arsenic-containing wastewater (HSAW). In this study, for the first time, Me (Ca and Mg)-AsO-OH-HO and Me (Ca and Mg)-AsO-CO-HO systems are studied based on a thermodynamic equilibrium diagram and an arsenic removal experiment, proving that the removal of arsenic using single metal ions in the presence of CO is infeasible because of carbonate coprecipitation. Based on this observation, a new method that uses magnesium ammonium complex salts (MACSs) for HSAW treatment is proposed.

Nondestructive detection of total soluble solids in grapes using VMD-RC and hyperspectral imaging.

Total soluble solids (TSS) are one of the most essential attributes determining the quality and price of fruit. This study aimed to use hyperspectral imaging (HSI) and wavelength selection for nondestructive detection of TSS in grape. A novel method involving variational mode decomposition and regression coefficients (VMD-RC) was proposed to select feature wavelengths.

Research on nondestructive identification of grape varieties based on EEMD-DWT and hyperspectral image.

Grape varieties are directly related to the quality and sales price of table grapes and consumed products (raisin, wine, grape juice, etc.). To satisfy the identification requirements of rapid, accurate, and nondestructive detection, an improved denoising algorithm based on ensemble empirical mode decomposition (EEMD) and discrete wavelet transform (DWT) is proposed to couple with the hyperspectral image (HSI) of grape varieties in this study.