Rapid identification and determination of adulteration in medicinal Arnebiae Radix by combining near infrared spectroscopy with chemometrics

被引:0
|
作者
Li, Xiaolong [1 ]
Zhong, Yongqi [1 ]
Jiaqi, Li [1 ]
Lin, Zhaozhou [2 ]
Pei, Yanling [3 ]
Dai, Shengyun [4 ]
Fei, Sun [1 ]
机构
[1] Guangdong Pharmaceut Univ, Sch Chinese Mat Med, Guangzhou, Peoples R China
[2] Beijing Zhongyan Tongrentang Med R&D Co Ltd, Beijing, Peoples R China
[3] Hebei Xinminhe Pharmaceut Technol Dev Co Ltd, Shijiazhuang, Hebei, Peoples R China
[4] Natl Inst Food & Drug Control, Beijing, Peoples R China
关键词
Arnebiae Radix; Near infrared spectroscopy; Data driven-soft independent modeling by class; analogy; Partial least squares-discriminant analysis; Partial least squares; Support vector machine; REGRESSION; PLS; CLASSIFICATION; NIR;
D O I
10.1016/j.saa.2024.124437
中图分类号
O433 [光谱学];
学科分类号
0703 ; 070302 ;
摘要
The medicinal Arnebia Radix (AR) is one of widely-used Chinese herbal medicines (CHMs), usually adulterated with non-medicinal species that seriously compromise the quality of AR and affect patients' health. Detection of these adulterants is usually performed by using expensive and time-consuming analytical instruments. In this study, a rapid, non-destructive, and effective method was proposed to identify and determine the adulteration in the medicinal AR by near-infrared (NIR) spectroscopy coupled with chemometrics. 37 batches of medicinal AR samples originated from Arnebia euchroma (Royle) Johnst., 11 batches of non-medicinal AR samples including Onosma paniculatum Bur. et Franch and Arnebia benthamii (Wall. ex G. Don) Johnston, and 72 batches of adulterated AR samples were characterized by NIR spectroscopy. The data driven-soft independent modeling by class analogy (DD-SIMCA) and partial least squares-discriminant analysis (PLS-DA) were separately used to differentiate the authentic from adulterated AR samples. Then the PLS and support vector machine (SVM) were applied to predict the concentration of the adulteration in the adulterated AR samples, respectively. As a result, the classification accuracies of DD-SIMCA and PLS-DA models were 100% for the calibration set, and 96.7% vs. 100% for the prediction set. Moreover, the relative prediction deviation (RPD) values of PLS models reached 11.38 and 7.75 for quantifying two adulterants species, which were obviously superior to the SVM models. It can be concluded that the NIR spectroscopy coupled with chemometrics is feasible to identify the authentic from adulterated AR samples and quantify the adulteration in adulterated AR samples.
引用
收藏
页数:11
相关论文
共 50 条
  • [1] Identification and Quantification of Turmeric Adulteration in Egg-Pasta by Near Infrared Spectroscopy and Chemometrics
    Biancolillo, Alessandra
    Santoro, Angela
    Firmani, Patrizia
    Marini, Federico
    APPLIED SCIENCES-BASEL, 2020, 10 (08):
  • [2] Near infrared spectroscopy for simultaneous quantification of five chemical components in Arnebiae Radix (AR) with partial least squares and support vector machine algorithms
    Zhong, Yong-Qi
    Li, Jia-Qi
    Li, Xiao-Long
    Dai, Sheng-Yun
    Sun, Fei
    VIBRATIONAL SPECTROSCOPY, 2023, 127
  • [3] A rapid determination of wheat flours components based on near infrared spectroscopy and chemometrics
    Zhou, Wanzhu
    Lei, Yongqian
    Zhou, Qidong
    Xu, Jingwei
    Xun, He
    Xu, Chunhua
    VIBRATIONAL SPECTROSCOPY, 2024, 130
  • [4] Rapid discrimination of adulteration in Radix Astragali combining diffuse reflectance mid-infrared Fourier transform spectroscopy with chemometrics
    Yang, Jun
    Yin, Chunling
    Miao, Xu
    Meng, Xiangru
    Liu, Zhimin
    Hu, Leqian
    SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY, 2021, 248
  • [5] Near infrared spectroscopy combined with chemometrics to detect and quantify adulteration of maca powder
    Zeng, Miao-Na
    Zheng, Shao-Yan
    JOURNAL OF NEAR INFRARED SPECTROSCOPY, 2021, 29 (02) : 108 - 115
  • [6] Rapid identification and quantitative pit mud by near infrared Spectroscopy with chemometrics
    Ning, Yu
    Zhang, Huimin
    Zhang, Qiang
    Zhang, Xueru
    VIBRATIONAL SPECTROSCOPY, 2020, 110
  • [7] Near-infrared spectroscopy with chemometrics for identification and quantification of adulteration in high-quality stingless bee honey
    Raypah, Muna E.
    Zhi, Loh Jing
    Loon, Lim Zi
    Omar, Ahmad Fairuz
    CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 2022, 224
  • [8] Portable near-infrared (NIR) spectrometer and chemometrics for rapid identification of butter cheese adulteration
    Medeiros, Maria Lucimar da Silva
    Lima, Adriano Freitas
    Goncalves, Monica Correia
    Godoy, Helena Teixeira
    Barbin, Douglas Fernandes
    FOOD CHEMISTRY, 2023, 425
  • [9] Rapid identification and quantification of Panax notoginseng with its adulterants by near infrared spectroscopy combined with chemometrics
    Liu, Peng
    Wang, Jing
    Li, Qian
    Gao, Jun
    Tan, Xiaoyao
    Bian, Xihui
    SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY, 2019, 206 : 23 - 30
  • [10] A rapid identification of four medicinal chrysanthemum varieties with near infrared spectroscopy
    Han, Bangxing
    Yan, Hui
    Chen, Cunwu
    Yao, Houjun
    Dai, Jun
    Chen, Naifu
    PHARMACOGNOSY MAGAZINE, 2014, 10 (39) : 353 - 358