Chemical Papers

, Volume 73, Issue 4, pp 1003–1012 | Cite as

Rapid quantification of analog complex using partial least squares regression on mass spectrum

  • Qianqian Li
  • Yue HuangEmail author
  • Kuangda Tian
  • Shungeng Min
  • Chunming Hao
Original Paper


Gas chromatograph combining mass spectrometry (GC–MS) has been widely applied to separate complicated complex and quantify specific components. But it was usually time and energy consuming, plus indispensably needed establishing the quantitation curves. To provide an alternative, a new application of the accumulation mass spectra (MS) combined with partial least squares (PLS) regression was used to determine the compositions of analog complex. An analog system with five organic reagents in random proportion and a complex system with 63 botanic leaf samples were detected by GC–MS. After accumulating mass spectra of the retention time on each sample, a serious of tri-dimensional data were created. Based on the chemometrical treatment of mass spectrum, satisfactory results of five chemicals and the nicotine concentration were obtained as the coefficients of determination (R2) of 93.59, 95.99, 98.87, 96.06, 96.02 and 89.16%, and the residual prediction deviation (RPD) of 5.25, 3.91, 4.42, 6.43, 3.35 and 3.26, respectively. Moreover, it was found that low concentration component can be quantified with high sensitivity by specifying the suitable mass-to-charge ratio range from the data matrix. In this study, the proposed combination method was proved to be a feasible, convenient and potential tool for the determination of complex in the future.

Graphical abstract


Mass spectra Partial least squares Chemometrics Characteristic peak 



This research was supported by the Fundamental Research Funds for the Central Universities of China (Nos: 2652015164; 3142017100), Langfang Technology Research and Development Program (No: 2017013131), and Key Laboratory of Mine Geological Hazards Mechanism and Control Project (KF2017-13).


  1. Barman I, Kong CR, Dingari NC, Dasari RR, Feld MS (2010) Development of robust calibration models using support vector machines for spectroscopic monitoring of blood glucose. Anal Chem 82:9719–9726CrossRefGoogle Scholar
  2. Chen QS, Pei J, Zhao JW (2010) Measurement of total flavone content in snow lotus (Saussurea involucrata) using near infrared spectroscopy combined with interval PLS and genetic algorithm. Spectrochim Acta A 76:50–55CrossRefGoogle Scholar
  3. Chen Y, Wang Q, Tang J, Zhang Z (2013) Determination of surface characteristics of epoxidized soybean oil by inverse GC. Chromatographia 76:91–96CrossRefGoogle Scholar
  4. Colombani C, Croiseau P, Fritz S, Guillaume F, Legarra A, Ducrocq V, robert-Granié C (2012) A comparison of partial least squares (PLS) and sparse PLS regressions in genomic selection in French dairy cattle. J Dairy Sci 95:2120–2131CrossRefGoogle Scholar
  5. Deng B, Yun Y, Liang Y (2014) A novel variable selection approach that iteratively optimizes variable space using weighted binary matrix sampling. Analyst 139:4836–4845CrossRefGoogle Scholar
  6. Du Y, Liang Y, Jiang J (2004) Spectral regions selection to improve prediction ability of PLS models by changeable size moving window partial least squares and searching combination moving window partial least squares. Anal Chim Acta 501:183–191CrossRefGoogle Scholar
  7. Jiang H, Liu G, Mei CL (2012) Measurement of process variables in solid-state fermentation of wheat straw using FT-NIR spectroscopy and synergy interval PLS algorithm. Spectrochim Acta Part A 97:277–283CrossRefGoogle Scholar
  8. Kokaly RF, Clark RN (1999) Spectroscopic determination of leaf biochemistry using band-depth analysis of absorption features and stepwise multiple linear regression. Remote Sens Environ 67:267–287CrossRefGoogle Scholar
  9. Lavine B, Workman J (2004) Chemometrics. Anal Chem 82:4137–4145Google Scholar
  10. Leardi R, Norgaard L (2004) Sequential application of backward interval partial least squares and genetic algorithms for the selection of relevant spectral regions. J Chemometr 18:486–497CrossRefGoogle Scholar
  11. Li J, Purves RW, Richards JC (2004) Coupling capillary electrophoresis and high-field asymmetric waveform ion mobility spectrometry mass spectrometry for the analysis of complex lipopolysaccharides. Anal Chem 76:4676–4683CrossRefGoogle Scholar
  12. Li C, Zhao T, Li C (2017) Determination of gossypol content in cottonseeds by near infrared spectroscopy based on Monte Carlo uninformative variable elimination and nonlinear calibration methods. Food Chem 221:990–996CrossRefGoogle Scholar
  13. Massart DL, Vandeginste BM, Buydens LC, Jong S, Lewi PJ, Smeyers-Verbeke J (1992) Handbook of chemometrics and qualimetrics part A. Elsevier, LondonGoogle Scholar
  14. Orzel J, Daszykowski M, Grabowski I, Zaleszczyk G, Sznajder M, Walczak B (2012) Simultaneous determination of Solvent Yellow 124 and Solvent Red 19 in diesel oil using fluorescence spectroscopy and chemometrics. Talanta 101:78–84CrossRefGoogle Scholar
  15. Perissinato AG, Garcia JS, Trevisan MG (2017) Determination of β-galactosidase in tablets by infrared spectroscopy. Chem Pap 71:171–176CrossRefGoogle Scholar
  16. Shariati-Rad M, Hasani M (2010) Selection of individual variables versus intervals of variables in PLSR. J Chemometr 24:45–56Google Scholar
  17. Tang G, Huang Y, Tian KD (2014) A new spectral variable selection pattern using competitive adaptive reweighted sampling combined with successive projections algorithm. Analyst 139:4894–4902CrossRefGoogle Scholar
  18. Tres A, Veer GD, Perez MD, Ruth SM (2012) Authentication of organic feed by near-infrared spectroscopy combined with chemometrics: a feasibility study. J Agric Food Chem 60:8129–8133CrossRefGoogle Scholar
  19. Vanloot P, Dupuy N, Guiliano M, Artaud J (2012) Characterisation and authentication of A. senegal and A. seyal exudates by infrared spectroscopy and chemometrics. Food Chem 135:2554–2560CrossRefGoogle Scholar
  20. Xu L, Ye Z, Cui H, Yu X, Cai C, Yang H (2012) Calibrating the shelf-life of Chinese flavored dry tofu by FTIR spectroscopy and chemometrics: effects of data preprocessing and nonlinear transformation on multivariate calibration accuracy. Food Anal Method 5:1328–1334CrossRefGoogle Scholar
  21. Yang Y, Chen J, Shi YP (2012) Determination of diethylstilbestrol in milk using carbon nanotube-reinforced hollow fiber solid-phase microextraction combined with high-performance liquid chromatography. Talanta 97:222–228CrossRefGoogle Scholar
  22. Ye SF, Wang D, Min SG (2008) Successive projections algorithm combined with uninformative variable elimination for spectral variable selection. Chemometr Intel Lab Syst 91:194–199CrossRefGoogle Scholar
  23. Zheng K, Li Q, Wang J (2012) Stability competitive adaptive reweighted sampling (SCARS) and its applications to multivariate calibration of NIR spectra. Chemometr Intel Lab Syst 112:48–54CrossRefGoogle Scholar

Copyright information

© Institute of Chemistry, Slovak Academy of Sciences 2018

Authors and Affiliations

  • Qianqian Li
    • 1
  • Yue Huang
    • 2
    Email author
  • Kuangda Tian
    • 3
  • Shungeng Min
    • 3
  • Chunming Hao
    • 2
  1. 1.School of Marine ScienceChina University of GeoscienceBeijingPeople’s Republic of China
  2. 2.North China Institute of Science and TechnologyHebeiPeople’s Republic of China
  3. 3.China Agricultural UniversityBeijingPeople’s Republic of China

Personalised recommendations