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利用近红外光谱法快速鉴别四种药用菊花品种

A rapid identification of four medicinal chrysanthemum varieties with near infrared spectroscopy.

作者信息

Han Bangxing, Yan Hui, Chen Cunwu, Yao Houjun, Dai Jun, Chen Naifu

机构信息

College of Biological and Pharmaceutical Engineering, West Anhui University, Anhui Province, Lu'an, People's Republic of China ; Engineering Technology Research Center of Plant Cell Engineering, Anhui Province, Lu'an, People's Republic of China.

School of Biological and Environmental Engineering, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu, People's Republic of China.

出版信息

Pharmacogn Mag. 2014 Jul;10(39):353-8. doi: 10.4103/0973-1296.137378.

DOI:10.4103/0973-1296.137378
PMID:25210325
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4159931/
Abstract

BACKGROUND

For genuine medicinal material in Chinese herbs; the efficient, rapid, and precise identification is the focus and difficulty in the filed studying Chinese herbal medicines. Chrysanthemum morifolium as herbs has a long planting history in China, culturing high quality ones and different varieties. Different chrysanthemum varieties differ in quality, chemical composition, functions, and application. Therefore, chrysanthemum varieties in the market demands precise identification to provide reference for reasonable and correct application as genuine medicinal material.

MATERIALS AND METHODS

A total of 244 batches of chrysanthemum samples were randomly divided into calibration set (160 batches) and prediction set (84 batches). The near infrared diffuses reflectance spectra of chrysanthemum varieties were preprocessed by first order derivative (D1) and autoscaling and was built model with partial least squares (PLS).

RESULTS

In this study of four chrysanthemum varieties identification, the accuracy rates in calibration sets of Boju, Chuju, Hangju, and Gongju are respectively 100, 100, 98.65, and 96.67%; while the accuracy rates in prediction sets are 100% except for 99.1% of Hangju.

CONCLUSION

The research results demonstrate that the qualitative analysis can be conducted by machine learning combined with near infrared spectroscopy (NIR), which provides a new method for rapid and noninvasive identification of chrysanthemum varieties.

摘要

背景

对于中药材正品而言，高效、快速且精准的鉴定是中药材研究领域的重点和难点。菊花作为中药材在中国有着悠久的种植历史，培育出了高品质的不同品种。不同菊花品种在品质、化学成分、功能及应用方面存在差异。因此，市场上的菊花品种需要精准鉴定，为其作为正品中药材的合理正确应用提供参考。

材料与方法

共244批次菊花样品被随机分为校正集（160批次）和预测集（84批次）。菊花品种的近红外漫反射光谱经一阶导数（D1）和自动标度预处理后，采用偏最小二乘法（PLS）建立模型。

结果

在本次对四个菊花品种的鉴定研究中，亳菊、滁菊、杭菊和贡菊在校正集的准确率分别为100%、100%、98.65%和96.67%；而预测集中除杭菊为99.1%外，其他均为100%。

结论

研究结果表明，结合近红外光谱（NIR）利用机器学习可进行定性分析，为菊花品种的快速无损鉴定提供了一种新方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/434b/4159931/24638b77e89c/PM-10-353-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/434b/4159931/c04f38e73fb2/PM-10-353-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/434b/4159931/18f0d0e3ef73/PM-10-353-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/434b/4159931/5a86148ffd21/PM-10-353-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/434b/4159931/e5f7e34bcd46/PM-10-353-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/434b/4159931/7d66ca87013a/PM-10-353-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/434b/4159931/496f8b57421d/PM-10-353-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/434b/4159931/9074c0ef7747/PM-10-353-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/434b/4159931/69c5b8de9061/PM-10-353-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/434b/4159931/24638b77e89c/PM-10-353-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/434b/4159931/c04f38e73fb2/PM-10-353-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/434b/4159931/18f0d0e3ef73/PM-10-353-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/434b/4159931/5a86148ffd21/PM-10-353-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/434b/4159931/e5f7e34bcd46/PM-10-353-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/434b/4159931/7d66ca87013a/PM-10-353-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/434b/4159931/496f8b57421d/PM-10-353-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/434b/4159931/9074c0ef7747/PM-10-353-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/434b/4159931/69c5b8de9061/PM-10-353-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/434b/4159931/24638b77e89c/PM-10-353-g009.jpg

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