Predicting spice mixture composition: Comparing electronic nose, gas chromatography, and sensory methods

Electronic noses (e-nose) are instruments that can quickly detect odors at low cost, and their potential applications are very diverse. Limited work has been done in investigating e-noses' quantitative ability and no work has been reported on the application of e-noses to predict mixture compositions.; The objective of this project was to develop a quantitative procedure that could quickly predict the compositions of a ternary spice mixture by using an e-nose for measurement and multivariate statistics/neural networks (NN) for data analysis. The relative accuracy and efficiency of the developed e-nose methods were determined by comparing them to those resulting from gas chromatography (GC) and sensory methods.; Three ground spices (basil, cinnamon and garlic) were mixed in different compositions and presented to an e-nose. Nineteen training blends were used to build a predictive model, the performance of which was tested by five other blends. Three NN structures were used for predictive model building (multilayer perceptron (MLP), MLP using principal components analysis as preprocess and time-delay NN). For GC analysis, the volatile components of spice mixtures were collected by simultaneous distillation-extraction and quantified by GC. Mixture compositions were predicted based on the amounts of unique volatiles of each spice. The testing blends applied in GC analysis were the same as those in e-nose experiments. For sensory analysis, triangle tests were performed by 50 panelists to estimate the difference thresholds of spice mixtures.; The best NN model built from e-nose data predicted the compositions of testing spice mixtures with an error less than 0.06. A difference of 0.06 between two spice mixtures was determined through sensory analysis to be lower than human sensory thresholds. The GC method provided a more accurate but less efficient prediction. Its experimental time required for each unknown sample was 8 hours, instead of 50 minutes for the e-nose method.; The procedure developed in this study can predict the compositions of a ternary spice mixture with an acceptable accuracy and significantly improved efficiency. The procedure will be valuable in quality monitoring or process control, in which efficiency is essential.