The symbiont formed by legumes and rhizobia plays a crucial role in the nitrogen cycle， and root exudates significantly affect the nodulation process. The pretreatment and detection methods for metabolites of rhizobia from Lespedeza daurica have not been systematically optimized. In this study， the extraction efficiency of rhizobial metabolites was improved by optimizing pretreatment conditions， including ultrasonic temperature （A）， ultrasonic time （B）， derivatization temperature （C）， and derivatization time （D）. Single-factor experiments were used to determine the ranges of detection conditions， followed by a four-factor， three-level Box-Behnken response surface methodology （ultrasonic temperature： 30-40℃ ultrasonic time： 7-11 min derivatization time： 20-100 min derivatization temperature： 50-90℃） for optimization design. A quadratic regression model was established to determine the optimal conditions and verify their reliability. The optimal conditions determined by single-factor experiments were ultrasonic temperature 35℃， ultrasonic time 9 min， derivatization temperature 70℃， and derivatization time 60 min. The response surface model （R²=0.9035） showed that the linear terms A and C， the interaction term CD， and the quadratic terms A²， C²， and D² had extremely significant effects on the results （P<0.01）， while the interaction term AD had a significant effect （P<0.05）. After optimization， 145 metabolites were detected， representing an increase of 198.60% compared with the pre-optimization level， with an error of 3.5%. Methodological validation showed that the peak area RSD of the internal standard L-2-chloro-phenylalanine was 8.2% （n=6）， and the signal-to-noise ratio （S∶N） was 9.9∶1， which met the requirements for metabolomic detection. This study established a pretreatment method for the detection of metabolites from rhizobia of Lespedeza daurica， providing a technical basis for their extraction and analysis.

Lespedeza daurica  /  Metabolites of Rhizobia  /  Optimization of pretreatment methods