A novel simultaneous quantitative method for differential volatile components in herbs based on combined near-infrared and mid-infrared spectroscopy

被引:12
|
作者
Fan, Yao [2 ]
Bai, Xiuyun [1 ]
Chen, Hengye [1 ]
Yang, Xiaolong [1 ]
Yang, Jian [3 ]
She, Yuanbin [2 ]
Fu, Haiyan [1 ]
机构
[1] South Cent Minzu Univ, Coll Pharm, Modernizat Engn Technol Res Ctr Ethn Minor Med Hub, Wuhan 430074, Peoples R China
[2] Zhejiang Univ Technol, Coll Chem Engn, Hangzhou 310032, Peoples R China
[3] China Acad Chinese Med Sci, Natl Resource Ctr Chinese Mat Med, State Key Lab Breeding Base Dao di Herbs, Beijing 100700, Peoples R China
基金
国家重点研发计划; 欧盟地平线“2020”; 中国国家自然科学基金;
关键词
Herbs; NIR-MIR; Differential volatile components; Simultaneous quantitative analysis; Chemometrics; MEDICINAL HERBS; SPICES; AUTHENTICATION; RAMAN;
D O I
10.1016/j.foodchem.2022.135096
中图分类号
O69 [应用化学];
学科分类号
081704 ;
摘要
A novel method based on GC-MS, near-infrared (NIR) and mid-infrared (MIR) spectroscopy combined with chemometrics was established to simultaneously analyze differential volatile components (DVCs) of herb sam-ples. Herein, Florists Chrysanthemum was adopted as the representative sample. Through the introduction of Automatic data analysis workflow (AntDAS) and one-class partial least squares discriminant analysis (O-PLSDA) model, five kinds of terpenes and five kinds of alcohols were efficiently screened as DVCs. By using the selected NIR-MIR spectra sections combined with O-PLSDA, it could achieve the accurate identification of Florists Chry-santhemum from Chrysanthemum morifolium Ramat. What's more, since the selected spectra sections were closely related to the structural and content of DVCs, they could be further used for simultaneous quantitative analysis of DVCs combined with optimized variable-weighted least-squares support vector machine based on particle swarm optimization (PSO-VWLS-SVM). This method only adopted the same NIR-MIR sections for multiple component accurate quantification, highlighting its convenience.
引用
收藏
页数:7
相关论文
共 50 条
  • [31] Infrared Spectroscopy-Mid-infrared, Near-infrared, and Far-infrared/Terahertz Spectroscopy
    Ozaki, Yukihiro
    ANALYTICAL SCIENCES, 2021, 37 (09) : 1193 - 1212
  • [32] Quantitative reflectance spectroscopy as an alternative to traditional wet lab analysis of foliar chemistry: near-infrared and mid-infrared calibrations compared
    Richardson, AD
    Reeves, JB
    CANADIAN JOURNAL OF FOREST RESEARCH-REVUE CANADIENNE DE RECHERCHE FORESTIERE, 2005, 35 (05): : 1122 - 1130
  • [33] Mid-infrared near-field spectroscopy
    Amarie, Sergiu
    Ganz, Thomas
    Keilmann, Fritz
    OPTICS EXPRESS, 2009, 17 (24): : 21794 - 21801
  • [34] The Application of Visible and Near-Infrared Spectroscopy Combined with Chemometrics in Classification of Dried Herbs
    Dankowska, Anna
    Majsnerowicz, Agnieszka
    Kowalewski, Wojciech
    Wlodarska, Katarzyna
    SUSTAINABILITY, 2022, 14 (11)
  • [35] Comparison of near-infrared, mid-infrared, Raman spectroscopy and near-infrared hyperspectral imaging to determine chemical, structural and rheological properties of apple purees
    Lan, Weijie
    Baeten, Vincent
    Jaillais, Benoit
    Renard, Catherine M. G. C.
    Arnould, Quentin
    Chen, Songchao
    Leca, Alexandre
    Bureau, Sylvie
    JOURNAL OF FOOD ENGINEERING, 2022, 323
  • [36] Near-infrared and mid-infrared integrated photonics based on Ge-on-insulator platform
    Takenaka, Mitsuru
    Kang, Jian
    Takagi, Shinichi
    30TH ANNUAL CONFERENCE OF THE IEEE PHOTONICS SOCIETY (IPC), 2017, : 105 - 106
  • [37] Rapid Recognition of Geoherbalism and Authenticity of a Chinese Herb by Data Fusion of Near-Infrared Spectroscopy (NIR) and Mid-Infrared (MIR) Spectroscopy Combined with Chemometrics
    Fu, Haiyan
    Shi, Qiong
    Wei, Liuna
    Xu, Lu
    Guo, Xiaoming
    Hu, Ou
    Lan, Wei
    Xie, Shunping
    Yang, Tianming
    JOURNAL OF SPECTROSCOPY, 2019, 2019
  • [38] Simultaneous Identification of Wheat Origin and Drying Degree Using Near-Infrared and Mid-Infrared Fusion Techniques
    Zou Xiao-bo
    Feng Tao
    Zheng Kai-yi
    Shi Ji-yong
    Huang Xiao-wei
    Sun Yue
    SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39 (05) : 1445 - 1450
  • [39] Data fusion of near-infrared and mid-infrared spectra for identification of rhubarb
    Sun, Wenjuan
    Zhang, Xin
    Zhang, Zhuoyong
    Zhu, Ruohua
    SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY, 2017, 171 : 72 - 79
  • [40] Converting mid-infrared signals to near-infrared through optomechanical transduction
    Kapsalis, A.
    Mesaritakis, C.
    Bogris, A.
    Syvridis, D.
    QUANTUM SENSING AND NANOPHOTONIC DEVICES XII, 2015, 9370