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目的:利用近红外光谱技术,建立红参提取过程中关键组分的定量模型,实现快速检测功能;以近红外光谱为基础,结合动力学方程,建立提取过程动态趋势模型,实现全过程预测功能。方法:在线采集红参提取液近红外光谱,以HPLC获取关键成分数据,使用最小二乘法(PLSR)建立红参总皂苷的定量模型;通过定量模型以及近红外光谱,结合传质动力学方程,拟合建立提取过程随时间的动态关系模型,实现提取过程预测。结果:红参总皂苷定量模型的校正集相关系数r、校正均方根误差RMSEC、预测均方根误差RMSEP分别为0.996 09,0.018 9,0.016 8;以红参提取一阶动力学方程结合NIR定量模型建立提取过程趋势预测模型,模型显示趋势预测性能良好,具有较高的精度。结论:近红外法获得的定量模型拥有较好的检测精度,能实现快速在线检测功能;所建立的全过程提取动力学方程与实际提取过程趋势较为契合,满足预测需求。
OBJECTIVE: To establish a quantitative model of key components in the process of red ginseng extraction by near-infrared spectroscopy to realize rapid detection function. Based on near infrared spectroscopy and kinetic equation, the dynamic trend model of extraction process was established to realize the whole process prediction function. Methods: Near infrared spectra of red ginseng extract were collected online. The key components were obtained by HPLC. The quantitative model of total ginsenoside was established by least square method (PLSR). Based on quantitative model and near infrared spectroscopy (NIRS) Fitting to establish a dynamic relationship between the extraction process over time model, to achieve the extraction process prediction. Results: The calibration set correlation coefficient r, root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP) were 0.996 09,0.018 9,0.016 8, respectively. The first order kinetic equation of red ginseng combined with NIR The quantitative model establishes the trend forecasting model of the extraction process. The model shows that the trend forecasting performance is good and has high precision. Conclusion: The quantitative model obtained by near infrared spectroscopy has good detection precision and can realize the rapid on-line detection function. The established whole-process extraction kinetic equation is in good agreement with the actual extraction process trend to meet the prediction requirement.