Wang Cheng, Ye Ju, Jiang Sisi, He Xuguang, Ma Min, Yin Li
School of Pharmacy, Qinghai Minzu University, Xining, Qinghai, China.
Northwest Institute of Plateau Biology, Chinese Academy of Sciences (CAS), Key Laboratory of Plant Resources of Qinghai-Tibet Plateau in Chemical Research, Xining, Qinghai, China.
Front Pharmacol. 2025 May 13;16:1547398. doi: 10.3389/fphar.2025.1547398. eCollection 2025.
Traditional Chinese medicine quality control faces challenges, lacking multidimensionality and reliable quantitative evidence. Comprehensive evaluation models based on external characteristics and multiple indicator metabolites are the future research direction. This study focuses on slices, aiming to establish a method for its quality evaluation.
The appearance traits of slices were quantified, and the contents of six functional metabolites were determined. With eight traits and six metabolite contents as variables, principal component analysis (PCA) and orthogonal partial least-squares discrimination analysis (OPLS-DA) were performed. A weighted TOPSIS-GRA fusion model was established by combining the technique for order preference by similarity to ideal solution (TOPSIS) and gray relation analysis (GRA).
The six metabolites showed good linear relationships (R > 0.9992) within their respective ranges, with an average recovery rate of 98.54% - 103.07% (relative standard deviation less than 1.64%). Precision, stability, and repeatability met the relevant standards. There were significant differences in traits and metabolite contents among slices from different habitats. OPLS-DA identified differential quality-affecting markers. PCA showed that the first three principal components contributed over 80% of the cumulative variance, and 16 batches of slices were clustered into three categories by origin. The weighted TOPSIS-GRA fusion model indicated significant quality differences among slices from different regions, consistent with PCA and OPLS-DA clustering results.
The established multi-index content determination method is accurate and reliable for detecting metabolites in slices. The PCA, OPLS-DA, and weighted TOPSIS-GRA fusion models are scientifically reliable. The correlation between appearance traits and product quality can be used to evaluate slices from different regions, which is of great significance for quality control and standardization of traditional Chinese medicine.
中药质量控制面临挑战，缺乏多维度和可靠的定量证据。基于外在特征和多种指标性代谢产物的综合评价模型是未来的研究方向。本研究聚焦于[药材名称]切片，旨在建立其质量评价方法。
对[药材名称]切片的外观性状进行量化，并测定六种功能性代谢产物的含量。以八个性状和六种代谢产物含量为变量，进行主成分分析（PCA）和正交偏最小二乘法判别分析（OPLS-DA）。通过结合逼近理想解排序法（TOPSIS）和灰色关联分析（GRA）建立加权TOPSIS-GRA融合模型。
六种代谢产物在各自范围内呈现良好的线性关系（R>0.9992），平均回收率为98.54%-103.07%（相对标准偏差小于1.64%）。精密度、稳定性和重复性均符合相关标准。不同产地的切片在性状和代谢产物含量上存在显著差异。OPLS-DA识别出影响质量的差异标志物。PCA显示前三个主成分贡献率超过80%，16批次切片按产地聚为三类。加权TOPSIS-GRA融合模型表明不同地区的切片质量存在显著差异，与PCA和OPLS-DA聚类结果一致。
所建立的多指标含量测定方法用于检测[药材名称]切片中的代谢产物准确可靠。PCA、OPLS-DA和加权TOPSIS-GRA融合模型科学可靠。外观性状与产品质量之间的相关性可用于评价不同地区的[药材名称]切片，这对中药质量控制和标准化具有重要意义。
