Veterinary drug residues in meat seriously harm human health. Rapid and accurate detection of veterinary drug residues is necessary to minimize contamination. Taking ofloxacin (OFX) residues in mutton as an example, the near-infrared hyperspectral imaging combined with explainable AI was used to evaluate the importance of feature wavelengths in the convolutional neural network-stacked sparse auto-encoder (CNN-SSAE) model for chemical properties. Based on this, the qualitative (residue identification-residue level identification) and quantitative detection of OFX residues in mutton was realized. The results showed that the accuracy of CNN-SSAE in identifying residue and residue level of OFX was 100% and 93.65%, respectively, and the correlation coefficients for validation (R 2 P ) in quantitative detection of OFX residue was 0.8980. In addition, SHapley Additive exPlanation (SHAP) values were used to identify feature wavelengths that contribute the most in the CNN-SSAE model, which effectively explained the quality attribute information that spectral and chemical values may improve the predicted results in the model decision process. The reliability of the CNN-SSAE model was evaluated by statistical validation methods (F-test and T-test). Finally, the visualization diagram of OFX content distribution was established. This study provides a method reference for explainability detection of veterinary drug residues.
Efficient recovery of rare earth elements (REEs) from wastewater is crucial for advancing resource utilization and environmental protection. Herein, a novel nitrogen-rich hydrogel adsorbent (PEI-ALG@KLN) was synthesized by modifying coated kaolinite-alginate composite hydrogels with polyethylenimine through polyelectrolyte interactions and Schiff’s base reaction. Various characterizations revealed that the high selective adsorption capacity of Ho (155 mg/g) and Nd (125 mg/g) on PEI-ALG@KLN is due to a combination of REEs (Lewis acids) via coordination interactions with nitrogen-containing functional groups (Lewis bases) and electrostatic interactions; its adsorption capacity remains more than 85 % after five adsorption-desorption cycles. In waste NdFeB magnet hydrometallurgical wastewater, the recovery rate of PEI-ALG@KLN for Nd and Dy can reach more than 93 %, whereas that of Fe is only 5.04 %. Machine learning prediction was used to evaluate adsorbent properties via different predictive models, with the random forest (RF) model showing superior predictive accuracy. The order of significance for adsorption capacity was pH > time > initial concentration > electronegativity > ion radius, as indicated by the RF model feature importance analysis and SHapley Additive exPlanations values. These results confirm that PEI-ALG@KLN has considerable potential in the selective extraction of REEs from wastewater.
危险品化学品经营许可证（带存储）