质量分析

数据融合法结合决策树优化东方草莓全草的地理溯源性研究*

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  • 1.西南民族大学药学院,成都 610041;
    2.邯郸市人民医院药剂科,邯郸 056001;
    3.西安市东关南街社区卫生服务中心,西安 710068
第一作者 Tel:18200561730; E-mail:371235417@qq.com
** Tel:13258159190; E-mail:wxl3232@sina.com

收稿日期: 2021-03-30

  网络出版日期: 2024-06-24

基金资助

* 西南民族大学中央高校基本科研业务费专项资金(2020NYB33);西南民族大学中央高校基本科研业务费专项资金优秀研究生培养工程项目(2021NYYXS20)

Data fusion method to improve geographical traceability of Fragaria orientalis Lozinsk. whole herb*

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  • 1. College of Pharmacy, Southwest Minzu University, Chengdu 610041, China;
    2. Department of Pharmacy, Handan People’s Hospital 056001,China;
    3. Xi’an Dongguan South Street Community Health Service Center, Xi’an 710068, China

Received date: 2021-03-30

  Online published: 2024-06-24

摘要

目的: 通过融合HPLC与ICP-MS的分析结果建立数据集,结合模式识别法对来自5个产地96批次的藏药东方草莓进行地理溯源性研究,以期为未知来源的东方草莓提供便捷有效的产地判别方法。方法: 采用HPLC建立东方草莓指纹图谱,并以LC-MS对各色谱峰进行成分归属,色谱峰信息作为数据集1;采用ICP-MS对东方草莓中21种无机元素进行含量测定,并以对数法建立无机元素指纹图谱,无机元素信息作为数据集2;二者数据融合作为数据集3。结合CA、PCA、PCA-LDA及C5.0决策树算法,对比3个数据集的地理追溯结果。结果: 数据融合法相较于单一技术所得数据集,CA、PCA和PCA-LDA均可使96批东方草莓成功归类。对数据融合法的PCA-LDA模型进行内部验证和外部验证,其正确分类率均大于88.3%,表明该模型可作为东方草莓的正确分类依据。C5.0决策树筛选出4个主要贡献变量,并获得树深度为3的分类规则,其十折交叉验证结果显示平均准确度为98.9%。结论: 数据融合法可提高不同产地东方草莓地理溯源的准确性,且决策树算法得出的分类规则可降低实际操作难度。以数据融合法结合决策树规则所得出的东方草莓地理分类机制为未知样品的产地来源提供新的鉴别依据,且操作简便,准确率高,有利于东方草莓的质量控制,也为其他药材品种溯源提供参考。

本文引用格式

张旭超, 党艺航, 付艺萱, 郭坤, 王晓玲, 王舒, 刘凤杰 . 数据融合法结合决策树优化东方草莓全草的地理溯源性研究*[J]. 药物分析杂志, 2022 , 42(5) : 845 -855 . DOI: 10.16155/j.0254-1793.2022.05.14

Abstract

Objective: To combine the data of HPLC and ICP-MS with the single technology data with the pattern recognition method to conduct a geographic tracing study of 96 batches of Fragaria orientalis Lozinsk. from 5 procinces, in order to provide a convenient and effective identification method for the unknown origin of Fragaria orientalis Lozinsk.. Methods: The fingerprint of Fragaria orientalis Lozinsk. was established by HPLC, the components of each chromatographic peak were assigned by LC-MS, and the chromatographic peak information was used as data set 1. ICP-MS was used to determine the contents of 21 inorganic elements in Fragaria orientalis Lozinsk., the logarithm Inorganic element fingerprint map was established by the method, and the inorganic element information was used as data set 2; the fusion of the two data was used as data set 3. Combine CA, PCA, PCA-LDA and C5.0 decision tree algorithms to compare the geographic traceability results of the three data sets. Results: Compared with the data set obtained by a single technique, CA, PCA and PCA-LDA of the data fusion method can correctly classify 96 batches of Fragaria Orientalis Lozinsk.. The PCA-LDA model of the data fusion method was verified internally and externally, and the correct classification rate was greater than 88.3%, The results showed that the model could be used as the correct basis for the classification of Fragaria Orientalis Lozinsk.. The C5.0 decision tree screened out 4 main contributing variables and obtained a classification rule with a tree depth of 3. The 10-fold cross-validation result showed that the average accuracy was 98.9%. Conclusion: The data fusion method can improve the accuracy of the geographical traceability of Orientalis Lozinsk. from different origins, and the classification rules derived from the decision tree algorithm can reduce the difficulty of actual operation. The geographical classification mechanism of Orientalis Lozinsk. based on the data fusion method and decision tree rules provides a new identification basis for the unknown sample origin, with simple operation and high accuracy, which is beneficial to the quality control. This study also provided reference for other medicinal materials.

参考文献

[1] 党艺航, 郭坤, 王晓玲, 等. 藏药草莓的本草考证[J].中药材, 2019,42(5):1188
DANG Y, GUO K, WANG XL, et al. Materia medica study of Tibetan strawberry[J].Chin Mater Med, 2019,42(5):1188
[2] 国家中医药管理局《中华本草》编委会. 中华本草·藏药卷[M].上海:上海科学技术出版社,2002:231
Editorial Board of Chinese Materia Medica, State Administration of Traditional Chinese Medicine. Chinese Materia Medica and Tibetan, Medicine Volume[M].Shanghai:Shanghai Science and Technology Press,2002:231
[3] 罗达尚. 新修晶珠本草[M].成都:四川科学技术出版社,2004:367
LUO DS. Newly Revised Jingzhu Bencao[M].Chengdu:Sichuan Science and Technology Press,2004:367
[4] GIAMPIERI F, ALVAREZ-SUAREZ JM, JOSÉ M, et al. Strawberry and human health:effects beyond antioxidant activity[J].J Agric Food Chem, 2014, 62(18):3867
[5] ALVAREZ-SUAREZ JM, DEKANSKI D, RISTIC S, et al. Strawberry polyphenols attenuate ethanol-induced gastric lesion in rats by activation of antioxidant enzymes and attenuation of MDA increase[J].Plos One, 2011, 6 (10):e25878
[6] LIU PP, ZHENG PC, GONG ZM, et al. Comparing characteristic aroma components of bead-shaped green teas from different regions using headspace solid-phase microextraction and gas chromatography-mass spectrometry/olfactometry combined with chemometrics[J].Eur Food Res Technol, 2020, 246(9):1703
[7] 袁玉伟, 张永志, 付海燕,等. 茶叶中同位素与多元素特征及其原产地PCA-LDA判别研究[J].核农学报, 2013, 27(1):47
YUAN YW, ZHANG YZ, FU HY, et al. Identification of isotopes and multi-element in tea and their origin PCA-LDA discrimination[J].J Nucl Agric Sci, 2013, 27(1):47
[8] 樊双喜, 钟其顶, 黄占斌,等. 基于非目标1H NMR指纹图谱技术验证中国葡萄酒原产地[J].食品与发酵工业, 2018, 44(2):187
FAN SX, ZHONG QD, HUANG ZB, et al. Verification of Chinese wine origin based on non-target 1H NMR fingerprinting technique[J].Food Ferment Ind, 2018, 44(2):187
[9] FORLEO T, ZAPPI A, GOTTARDI F, et al. Rapid discrimination of Italian Prosecco wines by head-space gas-chromatography basing on the volatile profile as a chemometric fingerprint[J].Eur Food Res Technol, 2020,246(5):1805
[10] 翟新房, 赵焕虎, 杨册,等. 基于液质联用-模式识别方法分析不同产地的绞股蓝皂苷[J].中草药, 2019,50(13):3193
ZHAI XF, ZHAO HH, YANG C, et al. Analysis of gynostemma saponins of different origins based on liquid-mass spectrogen-pattern recognition method[J].Chin Herb Med, 2019,50(13):3193
[11] 王方杰,王婷,罗芳梅,等. 基于GC-MS代谢组学技术的杜仲抗骨质疏松作用研究[J].中国中药杂志, 2020, 45(22):253
WANG FJ, WANG T, LUO FM, et al. Anti-osteoporosis effect of eucommia based on GC-MS metabolomics technology[J].China J Chin Mater Med, 2020, 45(22):253
[12] MA GC, ZHANG YB, ZHANG JY, et al. Determining the geographical origin of Chinese green tea by linear discriminant analysis of trace metals and rare earth elements:taking Dongting Biluochun as an example[J].Food Control, 2016,59:714
[13] QIE MJ, ZHANG B, LI Z, et al. Data fusion by ratio modulation of stable isotope, multi-element, and fatty acids to improve geographical traceability of lamb[J].Food Control, 2021, 120:1
[14] ZHAO Y, ZHANG B, GUO B, et al. Combination of multi-element and stable isotope analysis improved the traceability of chicken from four provinces of China[J].CyTA-J Food, 2016,14(2):1
[15] YANG Q, HE HD, LUO TY, et al. Establishment of an HPLC fingerprint method and its application in evaluating the overall change of organic matter in a complex environment:taking the settled house dust as an example[J].Chem Papers, 2020, 74(5):1551
[16] AABY K, EKEBERG D, SKREDE G. Characterization of phenolic compounds in strawberry (fragaria×ananassa) fruits by different HPLC detectors and contribution of individual compounds to total antioxidant capacity[J].J Agric Food Chem, 2007, 55(11):4395
[17] LA BARBERA G, CAPRIOTTI AL, CAVALIERE C, et al. Comprehensive polyphenol profiling of a strawberry extract (Fragaria×ananassa) by ultra-high-performance liquid chromatography coupled with high-resolution mass spectrometry[J].Anal Bioanal Chem, 2017, 409(8):2127
[18] KOLEY TK, KHAN Z, OULKAR D, et al. Profiling of polyphenols in phalsa (Grewia asiatica L) fruits based on liquid chromatography high resolution mass spectrometry[J].J Food Sci Technol, 2019,57(2):606
[19] JEON SH, KUPPUSAMY S, YOON, YE, et al. Are there as many essential and non-essential minerals in hydroponic strawberry (Fragaria ananassa L.) compared to those grown in soil?[J].Biol Trace Elem Res, 2019,187(2):562
[20] NIEMELÄ M, KOLA H, PERÄMÄKI P, et al. Comparison of microwave-assisted digestion methods and selection of internal standards for the determination of Rh, Pd and Pt in dust samples by ICP-MS[J].Microchim Acta, 2005, 150(34):211
[21] TOKALIOGLU S, DOKAN FK, KOPRU S, et al. ICP-MS multi-element analysis for determining the origin by multivariate analysis of red pepper flakes from three different regions of Turkey[J].Food Sci Technol, 2020, 103(1):301
[22] GUMUS ZP, CELENK VU, TEKIN S, et al. Determination of trace elements and stable carbon isotope ratios in virgin olive oils from Western Turkey to authenticate geographical origin with a chemometric approach[J].Eur Food Res Technol, 2017,243(10):1719
[23] FAN SX, ZHONG QD, HUANG ZB, et al. Verification of the geographical origin of Chinese wines based on non-targeted ~1H NMR fingerprinting[J].Food Ferment Ind, 2018, 44(2):187
[24] LIU Z, YUAN Y, ZHANG Y, et al. Geographical traceability of Chinese green tea using stable isotope and multi-element chemometrics[J].Rapid Commun Mass Spectrom, 2019, 33(8):778
[25] FILZMOSER P, LIEBMANN B, VARMUZA K. Repeated double cross validation[J].J Chemometrics, 2009, 23(4):160
[26] AHMADI E, WECKMAN GR, MASEL DT. Decision making model to predict presence of coronary artery disease using neural network and C5.0 decision tree[J].J Amb Intel Hum Comp, 2017, 9(4):999
[27] 刘莺迎. 决策树分类算法的分析和比较[J].图书情报导刊, 2008, 18(2):65
LIU YY. Analysis and comparison of decision tree classification algorithm[J].Libr Inf Guide, 2008, 18(2):65
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