Optimization of ultrasound-assisted extraction and HPLC fingerprinting of luteoloside and chlorogenic acid in Lonicera japonica leaves

This study aimed to optimize the ultrasound-assisted extraction (UAE) process for luteoloside from Lonicera japonica ( L. japonica ) leaves using chlorogenic acid as a co-monitored marker, and to establish a high-performance liquid chromatography (HPLC) fingerprint method for the quality assessment of leaf samples from different geographical origins and harvest periods, thereby providing a scientific basis for their quality control and rational utilization. Single-factor experiments were conducted to optimize key UAE parameters, including ethanol concentration, liquid-to-solid ratio, ultrasonic power, extraction time, and number of extraction cycles, with luteoloside yield as the response variable. An HPLC fingerprint was then established using chlorogenic acid and luteoloside as marker compounds. Similarity analysis was performed using the Similarity Evaluation System for Chromatographic Fingerprints of Traditional Chinese Medicines (2012 edition), while hierarchical cluster analysis (HCA), principal component analysis (PCA), Spearman correlation analysis, and orthogonal partial least squares-discriminant analysis (OPLS-DA) were carried out using SPSS 27.0, Python, and SIMCA-P v14.1. The optimal UAE conditions were determined as follows: 60% (v/v) ethanol as the extraction solvent, a liquid-to-solid ratio of 25:1 (mL/g), ultrasonic power of 300 W, an extraction time of 2.5 h, and two extraction cycles. The established HPLC fingerprint contained 15 common peaks, of which chlorogenic acid (common peak 6, retention time 10.89 min) and luteoloside (common peak 15, retention time 20.14 min) were unambiguously identified by comparison with reference standards. The similarity values between the fingerprints of 18 sample batches and the generated reference fingerprint ranged from 0.827 to 1.000, with 14 batches showing high similarity (≥ 0.980), indicating good overall chemical consistency. HCA based on the peak areas of all 15 common peaks classified the 18 samples into three major clusters, which was primarily driven by intrinsic content level and harvest period. PCA and OPLS-DA further confirmed the discrimination among samples with different chemical patterns, and this discrimination was primarily driven by harvest period and intrinsic content level, with geographical origin playing a secondary role and showing dimension-specific effects (longitude for chlorogenic acid, latitude for luteoloside). Spearman analysis revealed significant spatial correlations for chlorogenic acid (longitude) and luteoloside (latitude), whereas no individual common peak showed a significant monotonic correlation with collection time, although multivariate clustering aligned primarily with harvest period. The first two principal components (PC1 and PC2) explained 38.3% and 24.0% of the total variance, respectively. The primary OPLS-DA model exhibited excellent fit and predictive ability ( R ²Y = 0.836, Q ² = 0.738). Among the 18 batches analyzed, the sample from Jiulong Mountain, Shiyan, Hubei (S12) exhibited the highest luteoloside content (0.162%). The optimized UAE process showed good repeatability under the tested conditions. The established HPLC fingerprint method, combined with chemometric analysis, effectively characterizes the chemical profiles of L. japonica leaves and provides technical support for their quality control and value-added utilization.