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首页> 外文期刊>Spectrochimica acta, Part A. Molecular and biomolecular spectroscopy >Prediction the contents of fructose, glucose, sucrose, fructooligosaccharides and iridoid glycosides in Morinda officinalis radix using near-infrared spectroscopy
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Prediction the contents of fructose, glucose, sucrose, fructooligosaccharides and iridoid glycosides in Morinda officinalis radix using near-infrared spectroscopy

机译:使用近红外光谱预测Morinda Officinalis radix中果糖,葡萄糖,蔗糖,果寡糖和伊啶醇糖苷的含量

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Morindae officinalis radix (MOR) is a famous Chinese herbal medicine which has long history of use in medicine and food. MOR and MOR with steaming process (PMOR) are the most commonly used forms in in clinical and health care. In order to establish a fast and mostly nondestructive quality control method for MOR, 183 beaches of MOR samples and 20 beaches of PMOR samples were collected commercially from major producing areas in Guangdong, Fujian and Guangxi Provinces of China. To predict main components of MOR, a calibration model was established based on near-infrared spectroscopy with partial least square regression. The model was optimized by compared the parameters of root mean square error of prediction (RMSEP), root mean square error of cross validation (RMSECV), coefficient of correlation (R2) and ratio of performance to deviation (RPD). Comparative studies were performed to evaluate the performance of models by different spectra preprocessing methods and different data set. The results showed that the model performance was improved with standard normal variate spectra preprocessing methods and when the data set contained both MOR and PMOR samples. A few PMOR samples were added to MOR samples data set the model predictive performance could be improved. The contents of 14 components were predicted in MOR with lower RMSEP and RMSECV, and higher R2 and RPD, including fructose (12.8 mg/g, 16.3 mg/g, 0.9873, 10.10),,,,Flucose (728 mg/g, 8.73 mg/g, 0.9611, 6.21 sucrose (9.24 mg/g, 9.10 mg/g, 0.8419, 1.75), GF2(9.42 mg/g, 11.3 mg/g, 03526, 2.03), GE3(7.98 mg/g, 9.20 mg/g, 0.8756, 274), G14(6.81 mg/g, 8.93 mg/g, 0.8663, 3.06), G15(8.13 mg/g, 8.85 mg/g, 0.9001, 3.06), GF6(6.40 mg/ g, 6.95 mg/g, 0.9145, 3.27), G17(5.53 mg/g, 6.15 mg/g, 0.9195, 3.57), G18(5.40 mg/g, 6.02 mg/g, 0.9179, 3.31), GF9(3.00 mg/g,4.35 mg/g,0.9446, 5.03),GF10(4.08 mg/g, 5.34 mg/g, 0.8983, 3.62), GF11(8.97 mg/g, 7.70 mg/g, 0.8683, 2.01) and iridoid glycosides (412 mg/g, 5.51 mg/g, 0.8712, 2.43). The model established in this paper could predict 14 components of MOR. The results would provide a reference method for the quality control of Chinese medical materials and their process products. 2020 Elsevier B.V. All rights reserved.
机译:Morindae Officinalis Radix(Mor)是一家着名的中草药,在医学和食物中具有悠久的使用历史。 Mor和Mor与蒸汽过程(PMOR)是临床和医疗保健中最常用的形式。为了为Mor建立快速而大多是无损性质的质量控制方法,从广东,福建和广西省市的主要产区商业地区收集了183个Mor样品和20个海滩的PMOR样本。为了预测MOR的主要组成部分,基于近红外光谱来建立校准模型,其近红外光谱具有部分最小二乘回归。通过比较预测(RMSEP)的根均方误差的参数,交叉验证(RMSECV)的根均方误差,相关系数(R2)和性能与偏差(RPD)的比率进行了优化了该模型。进行比较研究以评估不同光谱预处理方法和不同数据集的模型的性能。结果表明,标准正常变化谱预处理方法以及当数据集包含MOR和PMOR样本时,改善了模型性能。将一些PMOR样本添加到MOR样品中,数据集数据集可以提高模型预测性能。以较低的RMSEP和RMSECV预测14种成分的含量,较高的R2和RPD,包括果糖(12.8mg / g,16.3mg / g,0.9873,10.10),,,氟糖(728mg / g,8.73 Mg / g,0.9611,6.21蔗糖(9.24mg / g,9.10mg / g,0.8419,1.75),GF2(9.42mg / g,11.3mg / g,03526,2.03),GE3(7.98mg / g,9.20 mg / g,0.8756,274),G14(6.81mg / g,8.93mg / g,0.866,3.06),G15(8.13mg / g,8.85mg / g,0.9001,3.06),GF6(6.40 mg / g,6.95 Mg / g,0.9145,3.27),G17(5.53 mg / g,6.15mg / g,0.9195,3.57),G18(5.40mg / g,6.02 mg / g,0.9179,3.31),GF9(3.00 mg / g, 4.35mg / g,0.946,5.03),GF10(4.08 mg / g,5.34 mg / g,0.8983,3.62),GF11(8.97mg / g,7.70 mg / g,0.8683,2.01)和虹膜糖苷(412 mg / g,5.51 mg / g,0.8712,2.43)。本文建立的模型可以预测14个组件。结果将为中国医疗材料的质量控制及其工艺产品提供参考方法。2020 Elsevier BV所有权利预订的。

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