title: Chemometric brand differentiation of commercial spices using direct analysis in real time mass spectrometry.
abstract: BACKGROUND
  Commercial spices represent an emerging class of fuels for improvised explosives. Being able to classify such spices not only by type but also by brand would represent an important step in developing methods to analytically investigate these explosive compositions. Therefore, a combined ambient mass spectrometric/chemometric approach was developed to quickly and accurately classify commercial spices by brand.
  
  
  METHODS
  Direct analysis in real time mass spectrometry (DART-MS) was used to generate mass spectra for samples of black pepper, cayenne pepper, and turmeric, along with four different brands of cinnamon, all dissolved in methanol. Unsupervised learning techniques showed that the cinnamon samples clustered according to brand. Then, we used supervised machine learning algorithms to build chemometric models with a known training set and classified the brands of an unknown testing set of cinnamon samples.
  
  
  RESULTS
  Ten independent runs of five-fold cross-validation showed that the training set error for the best-performing models (i.e., the linear discriminant and neural network models) was lower than 2%. The false-positive percentages for these models were 3% or lower, and the false-negative percentages were lower than 10%. In particular, the linear discriminant model perfectly classified the testing set with 0% error. Repeated iterations of training and testing gave similar results, demonstrating the reproducibility of these models.
  
  
  CONCLUSIONS
  Chemometric models were able to classify the DART mass spectra of commercial cinnamon samples according to brand, with high specificity and low classification error. This method could easily be generalized to other classes of spices, and it could be applied to authenticating questioned commercial samples of spices or to examining evidence from improvised explosives.
authors.affiliation: Barnett Institute of Chemical and Biological Analysis, Department of Chemistry and Chemical Biology, Northeastern University, Boston, MA, USA.
authors.forename: Matthew J
authors.initials: MJ
authors.lastname: Pavlovich
authors.affiliation: Barnett Institute of Chemical and Biological Analysis, Department of Chemistry and Chemical Biology, Northeastern University, Boston, MA, USA.
authors.forename: Emily E
authors.initials: EE
authors.lastname: Dunn
authors.affiliation: Barnett Institute of Chemical and Biological Analysis, Department of Chemistry and Chemical Biology, Northeastern University, Boston, MA, USA.
authors.forename: Adam B
authors.initials: AB
authors.lastname: Hall
mesh_terms: D002623:Chemistry Techniques, Analytical / Q000379:methods*; D016002:Discriminant Analysis; D013058:Mass Spectrometry / Q000379:methods*; D015203:Reproducibility of Results; D017365:Spices / Q000032:analysis* / Q000145:classification / Q000191:economics