YOUNG CONSUMER'S PREFERENCE RESPONSE TO KETCHUP PRODUCTS

Textural and Rheological Properties of Sliceable Ketchup

This study investigates the effect of different mixtures of gums [xanthan (Xa), konjac mannan (KM), gellan, and locust bean gum (LBG)] on the physical, rheological (steady and unsteady), and textural properties of sliceable ketchup. Each gum had an individually significant effect (p < 0.05) on viscosity; however, the addition of Xa in combination with other gums had a greater effect on viscosity. By increasing the use of Xa in ketchup formulations, the amount of syneresis decreased such that the lowest amount of syneresis related to the sample prepared with 50% Xa and 50% gellan. Although the use of different levels of gums did not have a significant effect on the brightness (L) and redness (a) indices (p < 0.05), the use of different ratios of gums had a significant effect (p < 0.05) on the yellowness (b) index. The effect of different levels of gums used had a significant effect only on firmness (p < 0.05), and their effects on other textural parameters were not statistically significant (p > 0.05). The ketchup samples produced had a shear-thinning behavior, and the Carreau model was the best model to describe the flow behavior. Based on unsteady rheology, G’ was higher than G” for all samples, and no crossover between G’ and G” was observed for any of the samples. The constant shear viscosity (η) was lower than the complex viscosity (η*), which showed the weak gel structure. The particle size distribution of the tested samples indicated the monodispersed distribution. Scanning electron microscopy confirmed the viscoelastic properties and particle size distribution.

Characterization of Key Aroma Compounds in a Commercial Rum and an Australian Red Wine by Means of a New Sensomics-Based Expert System (SEBES)-An Approach To Use Artificial Intelligence in Determining Food Odor Codes

Although to date more than 10 000 volatile compounds have been characterized in foods, a literature survey has previously shown that only 226 aroma compounds, assigned as key food odorants (KFOs), have been identified to actively contribute to the overall aromas of about 200 foods, such as beverages, meat products, cheeses, or baked goods. Currently, a multistep analytical procedure involving the human olfactory system, assigned as Sensomics, represents a reference approach to identify and quantitate key odorants, as well as to define their sensory impact in the overall food aroma profile by so-called aroma recombinates. Despite its proven effectiveness, the Sensomics approach is time-consuming because repeated sensory analyses, for example, by GC/olfactometry, are essential to assess the odor quality and potency of each single constituent in a given food distillate. Therefore, the aim of the present study was to develop a fast, but Sensomics-based expert system (SEBES) that is able to reliably predict the key aroma compounds of a given food in a limited number of runs without using the human olfactory system. First, a successful method for the quantitation of nearly 100 (out of the 226 known KFOs) components was developed in combination with a software allowing the direct use of the identification and quantitation data for the calculation of odor activity values (OAV; ratio of concentration to odor threshold). Using a rum and a wine as examples, the quantitative results obtained by the new SEBES method were compared to data obtained by applying an aroma extract dilution analysis and stable isotope dilution assays required in the classical Sensomics approach. A good agreement of the results was found with differences below 20% for most of the compounds considered. By implementing the GC × GC data analysis software with the in-house odor threshold database, odor activity values (ratio of concentration to odor threshold) were directly displayed in the software pane. The OAVs calculated by the software were in very good agreement with data manually calculated on the basis of the data obtained by SIDA. Thus, it was successfully shown that it is possible to characterize key food odorants with one single analytical platform and without using the human olfactory system, that is, by "artificial intelligence smelling".