ZIF-8吸附材料对雪菊中木犀草苷的富集及在微型近红外光谱分析中的应用EI北大核心CSCD

制备了SiO;@ZIF-8吸附剂并用于吸附雪菊中微量的木犀草苷,建立了基于特征变量筛选的微型近红外光谱分析方法。以木犀草苷为对象,研究了吸附剂的质量、pH、振荡时间对吸附效果的影响;在最佳条件下对富集了木犀草苷的吸附剂直接进行检测,探讨分别采用竞争自适应加权采样算法(CARS)、蒙特卡罗-无信息变量消除法(MC-UVE)和随机青蛙算法(RT)进行变量筛选,并用偏最小二乘法建立定量校正模型。研究发现,当吸附剂质量为0.20 g, pH 7时振荡20 min,木犀草苷吸附率可达到92%;经3种变量筛选后最优模型为CARS,CWT方法光谱预处理后,木犀草苷校正模型参考浓度和预测浓度两者之间的相关系数达到最佳的0.9700,预测回收率在85%~120%。SiO_(2)@ZIF-8吸附剂可以有效的富集雪菊中的木犀草苷,在CARS变量筛选下,通过微型近红外光谱可以实现雪菊中微量木犀草苷的测定。

Enrichment of luteoloside in chamomile by ZIF-8 adsorption materials and its application in micro-NIR spectroscopy

A simple method was used to prepare SiO2 @ ZIF-8 adsorbent for the adsorption of trace luteoloside in Chamomile, and a micro-near infrared (NIR) spectroscopic analysis method based on feature variable screening was established. The effects of adsorbent dosage, pH value and oscillation time on the adsorption of luteoloside were studied. Under the optimum conditions, micro-NIR spectroscopy was used to detect the adsorbent enriched with luteoloside. The spectrum was used by competitive adaptive reweighted sampling (CARS), Monte Carlo-uninformed variable elimination (MC-UVE) and random testing (RT) for variable selection, and partial least squares(PLS) was used to establish a quantitative correction model. The results showed that the adsorption efficiency of luteoloside could reach 92%,with the mass of adsorbent of 0. 20 g, the pH of 7 and oscillating for 20 min. After the screening of three variables, the optimal model was CARS. After the spectral pretreatment of CWT method, the correlation coefficient between the reference concentration and the predicted concentration of luteoloside in the corrected model reached 0. 9700, and the predicted recoveries were in the range of 85%-120%. The above results indicated that SiO2 @ ZIF-8 adsorbent can effectively enrich luteoloside in Chamomile, and the determination of luteoloside in Chamomile can be achieved by micro-NIR spectroscopy under the screening of CARS variable. © 2022, Youke Publishing Co., Ltd. All rights reserved.