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Title: A Ricin Forensic Profiling Approach Based on a Complex Set of Biomarkers

Abstract

A forensic method for the retrospective determination of preparation methods used for illicit ricin toxin production was developed. The method was based on a complex set of biomarkers, including carbohydrates, fatty acids, seed storage proteins, in combination with data on ricin and Ricinus communis agglutinin. The analyses were performed on samples prepared from four castor bean plant (R. communis) cultivars by four different sample preparation methods (PM1 – PM4) ranging from simple disintegration of the castor beans to multi-step preparation methods including different protein precipitation methods. Comprehensive analytical data was collected by use of a range of analytical methods and robust orthogonal partial least squares-discriminant analysis- models (OPLS-DA) were constructed based on the calibration set. By the use of a decision tree and two OPLS-DA models, the sample preparation methods of test set samples were determined. The model statistics of the two models were good and a 100% rate of correct predictions of the test set was achieved.

Authors:
ORCiD logo [1];  [2];  [1];  [1];  [2];  [1]
  1. Swedish Defence Research Agency, Umea (Sweden)
  2. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1430520
Report Number(s):
PNNL-SA-130526
Journal ID: ISSN 0039-9140; PII: S0039914018303102
Grant/Contract Number:  
AC0576RL01830
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Talanta
Additional Journal Information:
Journal Name: Talanta; Journal ID: ISSN 0039-9140
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES

Citation Formats

Fredriksson, Sten-Ake, Wunschel, David S., Lindstrom, Susanne Wiklund, Nilsson, Calle, Wahl, Karen, and Astot, Crister. A Ricin Forensic Profiling Approach Based on a Complex Set of Biomarkers. United States: N. p., 2018. Web. doi:10.1016/J.TALANTA.2018.03.070.
Fredriksson, Sten-Ake, Wunschel, David S., Lindstrom, Susanne Wiklund, Nilsson, Calle, Wahl, Karen, & Astot, Crister. A Ricin Forensic Profiling Approach Based on a Complex Set of Biomarkers. United States. doi:10.1016/J.TALANTA.2018.03.070.
Fredriksson, Sten-Ake, Wunschel, David S., Lindstrom, Susanne Wiklund, Nilsson, Calle, Wahl, Karen, and Astot, Crister. Wed . "A Ricin Forensic Profiling Approach Based on a Complex Set of Biomarkers". United States. doi:10.1016/J.TALANTA.2018.03.070.
@article{osti_1430520,
title = {A Ricin Forensic Profiling Approach Based on a Complex Set of Biomarkers},
author = {Fredriksson, Sten-Ake and Wunschel, David S. and Lindstrom, Susanne Wiklund and Nilsson, Calle and Wahl, Karen and Astot, Crister},
abstractNote = {A forensic method for the retrospective determination of preparation methods used for illicit ricin toxin production was developed. The method was based on a complex set of biomarkers, including carbohydrates, fatty acids, seed storage proteins, in combination with data on ricin and Ricinus communis agglutinin. The analyses were performed on samples prepared from four castor bean plant (R. communis) cultivars by four different sample preparation methods (PM1 – PM4) ranging from simple disintegration of the castor beans to multi-step preparation methods including different protein precipitation methods. Comprehensive analytical data was collected by use of a range of analytical methods and robust orthogonal partial least squares-discriminant analysis- models (OPLS-DA) were constructed based on the calibration set. By the use of a decision tree and two OPLS-DA models, the sample preparation methods of test set samples were determined. The model statistics of the two models were good and a 100% rate of correct predictions of the test set was achieved.},
doi = {10.1016/J.TALANTA.2018.03.070},
journal = {Talanta},
number = ,
volume = ,
place = {United States},
year = {Wed Mar 28 00:00:00 EDT 2018},
month = {Wed Mar 28 00:00:00 EDT 2018}
}

Journal Article:
Free Publicly Available Full Text
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