chemometric brand differentiation of commercial spices using direct analysis in real time mass spectrometry.最优化的品牌差异化的商业香料使用直接实时分析质谱分析精品.pdfVIP

Research Article Received: 1 December 2015 Revised: 8 February 2016 Accepted: 10 February 2016 Published online in Wiley Online Library Rapid Commun. Mass Spectrom. 2016, 30, 1123–1130 () DOI: 10.1002/rcm.7536 Chemometric brand differentiation of commercial spices using direct analysis in real time mass spectrometry Matthew J. Pavlovich, Emily E. Dunn and Adam B. Hall* Barnett Institute of Chemical and Biological Analysis, Department of Chemistry and Chemical Biology, Northeastern University, Boston, MA, USA RATIONALE: 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 trai