Abstract
A method that identifies products based on a composite mass spectrum using standard chemical library searching functions is presented. Composite mass spectra were collected by sampling the headspace of a product directly without separation prior to analysis by a mass spectrometer. A library of spectra for 51 products (5 soaps, 2 hand lotions, 4 potato chips, 4 ketchups, 2 peanut butters, 4 breath mints/ gums, 13 citrus juices, 1 bourbon, 3 onions, 5 colas, 3 coffees, 5 peppers) was generated, and 7 unknowns samples (17 runs total with replicates) were tested against the library. Eleven of the 17 unknown sample runs were correctly identified with the top rated library match, four were identified as the second best match, and 2 were not identified in the top two matches. This level of correct matching (15 of 17 as best or second best match) is encouraging, suggesting that this technique could be used on a larger scale for product identification. This technique requires fewer analyses, doesn't require advanced statistical knowledge, and uses widely known mass spectral library tools. A SIMCA model identified all 9 citrus product samples in a validation data set.
