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T. HANKEMEIER, Analytical Sciences, TNO Nutrition & Food Research, Utrechtseweg 48, Zeist, 3704 HE, Netherlands Predicting the quality of products is important for product and process optimization. In principle, often a group of components of a product is responsible for a certain property or attribute of interest. However, a product can consist of hundreds or thousands of constituents, and one has to find the relevant components. In recent years we have developed various holistic analytical methods that are capable of analyzing hundreds to thousands of components in a single run. Holistic analysis is the (comparative) non-biased determination of nearly all components in a broad range of polarity and molecular weight present in a sample. Such comprehensive analytical methods are generally based on gas chromatography (GC) or liquid chromatography (LC) coupled to mass spectrometric (MS) detection. Next, data pre-processing tools and multivariate data analysis (MVA) are applied to find the relevant correlations between the analytical data with certain properties of interest of the product. Our objective was to investigate whether holistic profiling could predict the off flavour development of beer in time. Beer samples were analyzed with LC-MS and, after derivatization, with GC and GC-MS. After appropriate data pre-processing, for most odour and taste attributes models could be developed to predict the flavour development successfully from the analytical profiles. For example, the correlation coefficients obtained using a PLS model for nonenal odor and taste were 0.95 and 0.87, respectively and better. These results suggest that holistic profiling and multivariate data analysis can be applied to predict the product quality from the ingredients used, or from the process parameters used. This holistic concept has proven to be a breakthrough to predict flavour development in food products and food (ingredient) processing. Examples are prediction of bitterness of a food product and prediction of spoilage of apple juice.
Session 97, Sensory Evaluation: Analytical testing
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