Analytical and Bioanalytical Chemistry

, Volume 401, Issue 3, pp 939–955 | Cite as

Identification of heparin samples that contain impurities or contaminants by chemometric pattern recognition analysis of proton NMR spectral data

  • Qingda Zang
  • David A. Keire
  • Lucinda F. Buhse
  • Richard D. Wood
  • Dinesh P. Mital
  • Syed Haque
  • Shankar Srinivasan
  • Christine M. V. Moore
  • Moheb Nasr
  • Ali Al-Hakim
  • Michael L. Trehy
  • William J. Welsh
Original Paper


Chemometric analysis of a set of one-dimensional (1D) 1H nuclear magnetic resonance (NMR) spectral data for heparin sodium active pharmaceutical ingredient (API) samples was employed to distinguish USP-grade heparin samples from those containing oversulfated chondroitin sulfate (OSCS) contaminant and/or unacceptable levels of dermatan sulfate (DS) impurity. Three chemometric pattern recognition approaches were implemented: classification and regression tree (CART), artificial neural network (ANN), and support vector machine (SVM). Heparin sodium samples from various manufacturers were analyzed in 2008 and 2009 by 1D 1H NMR, strong anion-exchange high-performance liquid chromatography, and percent galactosamine in total hexosamine tests. Based on these data, the samples were divided into three groups: Heparin, DS ≤ 1.0% and OSCS = 0%; DS, DS > 1.0% and OSCS = 0%; and OSCS, OSCS > 0% with any content of DS. Three data sets corresponding to different chemical shift regions (1.95–2.20, 3.10–5.70, and 1.95–5.70 ppm) were evaluated. While all three chemometric approaches were able to effectively model the data in the 1.95–2.20 ppm region, SVM was found to substantially outperform CART and ANN for data in the 3.10–5.70 ppm region in terms of classification success rate. A 100% prediction rate was frequently achieved for discrimination between heparin and OSCS samples. The majority of classification errors between heparin and DS involved cases where the DS content was close to the 1.0% DS borderline between the two classes. When these borderline samples were removed, nearly perfect classification results were attained. Satisfactory results were achieved when the resulting models were challenged by test samples containing blends of heparin APIs spiked with non-, partially, or fully oversulfated chondroitin sulfate A, heparan sulfate, or DS at the 1.0%, 5.0%, and 10.0% (w/w) levels. This study demonstrated that the combination of 1D 1H NMR spectroscopy with multivariate chemometric methods is a nonsubjective, statistics-based approach for heparin quality control and purity assessment that, once standardized, minimizes the need for expert analysts.


Contour plot from grid search of the optimal values of γ and C for the SVM model


Heparin Proton nuclear magnetic resonance (1H NMR) Pattern recognition Classification and regression tree (CART) Artificial neural network (ANN) Support vector machine (SVM) 


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Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • Qingda Zang
    • 1
    • 2
    • 3
  • David A. Keire
    • 4
  • Lucinda F. Buhse
    • 4
  • Richard D. Wood
    • 2
  • Dinesh P. Mital
    • 3
  • Syed Haque
    • 3
  • Shankar Srinivasan
    • 3
  • Christine M. V. Moore
    • 5
  • Moheb Nasr
    • 5
  • Ali Al-Hakim
    • 5
  • Michael L. Trehy
    • 4
  • William J. Welsh
    • 1
  1. 1.Department of Pharmacology, Robert Wood Johnson Medical SchoolUniversity of Medicine and Dentistry of New JerseyPiscatawayUSA
  2. 2.Snowdon, Inc.Monmouth JunctionUSA
  3. 3.Department of Health Informatics, School of Health Related ProfessionsUniversity of Medicine and Dentistry of New JerseyNewarkUSA
  4. 4.Division of Pharmaceutical AnalysisFood and Drug Administration, CDERSt. LouisUSA
  5. 5.Office of New Drug Quality AssessmentFood and Drug Administration, CDERSilver SpringUSA

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