The volatile profiles of products can be used to understand differences in composition including flavour or aroma. Some flavour compounds may be present at extremely low levels, but still be critical in terms of consumer preference or perception. Dynamic Headspace (DHS) has been shown to give good sensitivity for trace level analytes. In order to fully understand differences in products – particularly those made from natural ingredients a number of replicates are required, resulting in large data sets needing analysis. Statistical methods such as Principal Component Analysis (PCA) can help to interrogate large data sets. This application note describes the use of DHS along with Mass Profiler Professional (MPP) Agilent software to identify the key differences between a number of commercially obtained chocolate samples.
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Using Dynamic Headspace and Principal Component Analysis to find key differences between chocolate samples