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Quantifying Adulteration of Licorice With Maltodextrin by Liquid and Solid-State NMR
Three samples were analyzed to determine if liquid or solid-state NMR techniques could be utilized to quantify adulteration of licorice powders by maltodextrin. Samples analyzed were:
Maltodextrin, Licorice #1, Licorice #2
Licorice #1 and Licorice #2 were analyzed by a combination of liquid-state 1H and 13C NMR on a Varian Unity-300 spectrometer, and solid-state 13C NMR on a Varian UnityPlus 200 spectrometer. The resulting spectra are shown in the attached plots.
One of the Licorice samples is adulterated by maltodextrin to an unknown concentration, the other licorice sample is pure licorice. Which sample was which was not known during the analysis. Initially it was hoped that the addition of maltodextrin to the licorice would be readily observed as new peaks appearing in the spectrum of the licorice sample. However, it can be seen that in both the 1H and 13C NMR there is considerable overlap of the peaks in the spectra of pure licorice and maltodextrin.
When no observable maltodextrin peaks could be assigned it was decided to simply use the quantitative integral data from the regions of the spectrum where the maltodextrin overlaps with the licorice spectrum compared to the integrals obtained from regions solely assignable to licorice. In Tables 1-3 are the quantitative results for each of the experiments performed.
Table 1: 1H NMR Integral Regions
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Normalized on Reg 4 |
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Regions 1 and 2 contain maltodextrin/licorice peaks.
Regions 3 and 4 contain only licorice peaks …. Data was norma lized to region 4. The norma lization norma lizes the licorice signal intensity. Thus the increased intensity of regions 1 and 2 in sample #1 is indicative that this sample contains maltodextrin. Samples #1+ and #2+ were made by adding more maltodextrin to the samples. Sample #1+ contains a further 10.9 wt % maltodextrin, while sample #2+ contains 11.4 wt% maltodextrin. The values were used to calculate the maltodextrin content in sample #1.
The 1H analysis indicates that there is 3.3 wt% maltodextrin in sample #1
Table 2: 13C NMR Integral Regions
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Normalize on Region 7 |
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Regions 1-3 were common to licorice and maltodextrin signals, while regions 4-7 were exclusive to licorice signals. Normalization on region 7 sets the licorice at a norma lized intensity. Again the intensty of regions 1-3 increases from sample #2 to sample #1 indicating the presence of maltodextrin in sample #1.
Calculation indicates that there is 6.1 wt% maltodextrin in the sample.
Table 3: Solid-State 13C Integral Regions
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Solids 13C CPMAS |
Normalized to Reg 3 |
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Region 1 contains maltodextrin and licorice signals, while regions 2 and 3 contain only licorice signals.
Again, the intensity of region 1 increases from sample #2 to 31 upon norma lization of the licorice only region 3. This confirms the presence of maltodextrin in sample #1. Samples #2+ and #1+ were not analyzed by solid-state NMR. This 13C analysis is much faster than the liquid-state NMR and would be a plausible short cut to quantify maltodextrin content.
Upon completion of the analysis it was revealed that the adulteration value was 5% maltodextrin.
Process NMR Symposia to be held at EAS 2007
John Edwards of Process NMR Associates has organized and sponsored two symposium sessions at the Eastern Analytical Symposium in Somerset New Jersey, November 12-15, 2007. One session will focus on high-resolution process NMR and the other on applications of TD-NMR in process control. The speakers and talk titles are listed below. Check the EAS site for exact details on the date and time of the sessions (EAS website). If you are interested in attending and would like to submit a paper for presentation visit the EAS Abstract submission site.
Session Title: Process NMR Technology – High Resolution NMR
John Edwards, Process NMR Associates, “Introduction to NMR in Process Control”
Miko DeLevy, Qualion NMR Analyzers, “Standardizing and Stabilizing NMR Calibration Transfer”
Paul Giammatteo, NMR Process Systems, “More from the Barrel – On-line NMR Increases Diesel Production and Quality”
Marcus Trygstad, Invensys Process Systems, “Taking NMR into the Refining Process: Best Practices and Benefits”
Andreas Kaerner , Eli Lilly, “Get Your Head Out of the Sand: Use of Reaction-NMR to Better Understand Reactions in Process Development”
Veena Bansal, Indian Oil Corporation, “Direct Prediction of Gasoline Properties for Monitoring Refinery Processes by 1H NMR Spectroscopy”
Session Title: Process NMR Technology – TD-NMR
Harry Xie, Bruker Optics, “Recent Developments in Time-domain NMR and its Applications in Polymer Industry”
Vaughn Davis, Progression Inc, “Time Domain NMR: Uses and Contributions to Process Control”
YiQiao Song, Schlumberger-Doll, “Recent Progress of NMR and MRI in Petroleum Exploration”
Maziar Sardashti, ConocoPhillips, “Applications of TD NMR to Laboratory and On-line Polymer Analysis”
Sergey Kryuchkov, University of Calgary, “Challenges in Online Water Cut Monitoring of Heavy Oil Thermal Operations Using Low Field NMR”
Chris Borgia, Colgate Palmolive, “Benchtop Fluoride NMR: A Rapid QC/QA Method”
Trans Fat Analysis by NMR
A series of Trans Fat standards was purchased from AOCS. The ability of 1H and 13C NMR to predict Trans Fat Content as well as
Saturated, Poly-unsaturated, and Mono-unsaturated Fat Content
The data of the samples is presented in the table below:
PLS regression techniques were used to correlate 1H and 13C NMR spectral variation to the unsaturation level and type of unsaturation of the samples.
Processed 13C data is shown below:
1H NMR data is shown below:
The following correlations were obtained from the 13C NMR data.
NMR Analysis of Essential Oils – Example of Sri Lankan Citronella
The data below shows the ability of 13C NMR to assign the natural product distribution found in essential oils. Once assignment of the oil hgas been obtained by 13C NMR the 1H NMR can also be assigned. For QA/QC a benchtop 60 MHz system has enough resolution that authenticity of essential oils can be performed either visually of by PCA type analysis.
Ger – Geraniol GerAc – Geranyl Acetate iEugMe – Methylisoeugenol Bor – Borneol
aPin – alpha-pinene Lim – Limonene tOci – trans-beta-Ocimene Cen – Camphene
Cllo – Citronellol Clla – Citronellal GenD – Germacrene D aCal – Citral A (Geranial)
aTol – alpha-Terpiniol cOci – cis-beta-Ocimene Myr – Myrcene