目的 以熱毒寧注射液（Reduning Injection，RI）為研究對(duì)象，應(yīng)用近紅外光譜（near infrared spectroscopy，NIRS）法和折光率法，建立快速檢測(cè)金銀花提取和濃縮工序中間體總固體量的方法。方法 收集RI金銀花提取和濃縮工序中間體，采用NIRS法與折光率法分別建立2種中間體總固體量的檢測(cè)方法，對(duì)比2種方法檢測(cè)結(jié)果的準(zhǔn)確性。結(jié)果 2種中間體的NIRS、折光率與總固體量之間均有強(qiáng)相關(guān)性，建立的總固體量定量預(yù)測(cè)模型的相關(guān)系數(shù)均大于0.97；模型驗(yàn)證結(jié)果顯示，金銀花濃縮工序中，NIRS模型和折光率模型預(yù)測(cè)性能相近，相對(duì)預(yù)測(cè)誤差均小于5%，表明針對(duì)金銀花濃縮工序，2種方法均可用于檢測(cè)總固體量；金銀花提取工序中，2種模型預(yù)測(cè)能力相差較大，NIRS模型預(yù)測(cè)準(zhǔn)確性較高，折光率模型預(yù)測(cè)準(zhǔn)確性較低，表明針對(duì)金銀花提取工序建立的折光率模型不適用，應(yīng)選用NIRS法檢測(cè)總固體量。結(jié)論 基于NIRS法與折光率法構(gòu)建的定量預(yù)測(cè)模型，實(shí)現(xiàn)金銀花提取和濃縮工序中間體總固體量的快速檢測(cè)，為RI生產(chǎn)過(guò)程質(zhì)量監(jiān)測(cè)選擇適宜的快速檢測(cè)技術(shù)提供了技術(shù)參考。

Objective Taking Reduning Injection (RI, 熱毒寧注射液) as the research object, the near-infrared spectroscopy (NIRS) method and the refractive index method were applied to establish a method for rapidly detecting the total solid content in the intermediates of the extraction and concentration processes of Jinyinhua (Lonicerae Japonicae Flos). Methods Intermediates from the extraction and concentration processes of Lonicerae Japonicae Flos in RI were collected. Two detection methods for the total solid content of these intermediates were respectively established using the NIRS method and the refractive index method, and the accuracy of both methods was compared. Results Both NIRS method and refractive index method demonstrated a strong correlation with the total solid content in the two intermediates, with correlation coefficients of the quantitative prediction models exceeding 0.97. The model validation results showed that the NIRS model and refractive index model had similar predictive performance in the concentration process of Lonicerae Japonicae Flos, with relative prediction errors of less than 5%. This indicated that both methods could be used to detect total solid content in this process. In the extraction process of Lonicerae Japonicae Flos, the predictive capabilities of the two models differed significantly, with the NIRS model showing higher prediction accuracy and the refractive index model showing lower prediction accuracy. This indicated that the refractive index model was not suitable for the extraction process of Lonicerae Japonicae Flos, and the NIRS method should be used to detect the total solid content. Conclusion Quantitative prediction models based on NIRS and refractive index methods were constructed for the rapid detection of total solid content in intermediates of the extraction and concentration processes of Lonicerae Japonicae Flos. This study provided a technical reference for selecting an appropriate rapid detection technology for the quality monitoring in the production process of RI.

R283.6

國(guó)家長(zhǎng)三角科技創(chuàng)新共同體聯(lián)合攻關(guān)項(xiàng)目（2023CSJGG1700）