基于NIRS技术的款冬花药材质控指标定量分析模型的建立 点击下载
| 论文标题： | 基于NIRS技术的款冬花药材质控指标定量分析模型的建立 |
| 英文标题： | |
| 中文摘要： | 目的 建立基于近红外光谱（NIRS）技术的款冬花药材中款冬酮、水分、醇溶性浸出物和总灰分含量的定量分析模型，为款冬花药材及其制剂的快速质量评价提供新思路。方法参照2020年版《中国药典》，分别采用高效液相色谱法、烘干法、热浸法及灰分测定法测定19个产地130批款冬花药材中主要质控指标款冬酮、水分、醇溶性浸出物、总灰分的含量，采集款冬花药材的NIRS数据信息，然后采用NIRS结合偏最小二乘法建立样品中上述质控指标的各个定量分析模型，经验证集样品验证后得到NIRS含量预测模型。结果130批款冬花药材样品中款冬酮、水分、醇溶性浸出物和总灰分的含量范围分别为0.0514%～0.1035%、7.75%～10.93%、20.17%～31.12%、7.68%～12.10%。所建立的款冬花药材中款冬酮、水分、醇溶性浸出物和总灰分定量分析模型的内部交叉验证决定系数（R2）分别为0.9858、0.9684、0.9734、0.9880；校正集均方差（RMSEC）分别为0.00154、0.187、0.478、0.127；预测均方差（RMSEP）分别为0.00181、0.212、0.543、0.149；RMSEP/RMSEC分别为1.1753、1.1337、1.1360、1.1732，均在合理范围内（1＜RMSEP/RMSEC≤1.2）。验证集样品中上述4个质控指标真实值与模型预测值的平均绝对误差分别为－0.00036、0.06143、0.14400和0.01043，平均预测回收率分别为99.65%、100.72%、100.66%和100.15%。结论所建NIRS定量分析模型稳定性好、测定结果可靠，可用于款冬花药材中相关质控指标含量的快速批量预测。 |
| 英文摘要： | OBJECTIVE To establish a quantitative analysis model for the contents of tussilagone， moisture， ethanol-soluble extract and total ash in Farfarae Flos based on near-infrared spectroscopy （NIRS）， providing a new idea for the rapid quality evaluation of Farfarae Flos and its preparations. METHODS Referring to the 2020 edition of the Chinese Pharmacopoeia， the contents of the main quality control indexes tussilagone， moisture， ethanol-soluble extract and total ash in 130 batches of Farfarae Flos from 19 producing areas were determined by HPLC， drying method， hot dip method and ash assay， respectively. The NIRS data information of the medicinal herbs of Farfarae Flos was collected， and then NIRS combined with the partial least squares method was used to establish the individual quantitative analysis models of the above quality control indexes in the samples， and the predictive model of the NIRS content was obtained after sample validation with validation set. RESULTS The range for the contents of tussilagone， moisture， ethanol-soluble extract and total ash in 130 batches of Farfarae Flos were 0.051 4%-0.103 5%， 7.75%-10.93%， 20.17%-31.12%， and 7.68%-12.10%， respectively. The internal cross-validation coefficients of determination （R2） of the established models for the quantitative analysis of tussilagone， moisture， ethanol-soluble extract and total ash in Farfarae Flos were 0.985 8， 0.968 4， 0.973 4， 0.988 0， respectively； the root mean square errors of calibration （RMSEC） were 0.001 54， 0.187， 0.478， 0.127， respectively； the root mean square errors of prediction （RMSEP） were 0.001 81， 0.212， 0.543， 0.149， respectively； RMSEP/RMSEC were 1.175 3， 1.133 7， 1.136 0 and 1.173 2， respectively， which were all within a reasonable range （1＜RMSEP/RMSEC≤1.2）. The mean absolute errors between the true and model-predicted values of the above four quality control indexes in the validation set of samples were －0.000 36， 0.061 43， 0.144 00， and 0.010 43， respectively，and the mean predicted recoveries were 99.65%， 100.72%，100.66%， and 100.15%， respectively. CONCLUSIONS The established NIRS quantitative analysis model has high stability and reliable results， which can be used for the rapid batch prediction of the content of relevant quality control indexes in Farfarae Flos. |
| 期刊： | 2024年第35卷第09期 |
| 作者： | 耿涛；蒋文慧；刘佳伦；兰松平；王柳璎；陈佩林；严寒静；姬生国 |
| 英文作者： | GENG Tao， JIANG Wenhui，LIU Jialun，LAN Songping，WANG Liuying，CHEN Peilin，YAN Hanjing，JI Shengguo |
| 关键字： | 款冬花；近红外光谱技术；款冬酮；快速分析；定量分析模型；质量评价 |
| KEYWORDS： | Farfarae Flos； near-infrared spectroscopy； tussilagone； rapid analysis； quantitative analysis model； quality evaluation |
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