Solid phase microextraction/gas chromatography/mass spectrometry integrated with chemometrics for detection of Salmonella typhimurium contamination in a packaged fresh vegetable.

A rapid method for detection of Salmonella typhimurium contamination in packaged alfalfa sprouts using solid phase microextraction/gas chromatography/mass spectrometry (SPME/GC/MS) integrated with chemometrics was investigated. Alfalfa sprouts were inoculated with S. typhimurium, packed into commercial LDPE bags and stored at 10+2 degrees C for 0, 1, 2 and 3 days. Uninoculated sprouts were used as control samples. A SPME device was used to collect the volatiles from the headspace above the samples and the volatiles were identified using GC/MS. Chemometric techniques including linear discriminant analysis (LDA) and artificial neural network (ANN) were used as data processing tools. Numbers of Salmonella were followed using a colony counting method. From LDA, it was able to differentiate control samples from sprouts contaminated with S. typhimurium. The potential to predict the number of contaminated S. typhimurium from the SPME/GC/MS data was investigated using multilayer perceptron (MLP) neural network with back propagation training. The MLP comprised an input layer, one hidden layer, and an output layer, with a hyperbolic tangent sigmoidal transfer function in the hidden layer and a linear transfer function in the output layer. The MLP neural network with a back propagation algorithm could predict number of S. typhimurium in unknown samples using the volatile fingerprints. Good prediction was found as measured by a regression coefficient (R(2)=0.99) between actual and predicted data.