Influence of Sous Vide and Water Immersion Processing on Polyacetylene Content and Instrumental Color of Parsnip (Pastinaca sativa) Disks

The effect of blanching (95 ± 3 °C) followed by sous vide (SV) processing (90 °C for 10 min) on levels of two polyacetylenes in parsnip disks immediately after processing and during chill storage was studied and compared with the effect of water immersion (WI) processing (70 °C for 2 min.). Blanching had the greatest influence on the retention of polyacetylenes in sous vide processed parsnip disks resulting in significant decreases of 24.5 and 24% of falcarinol (1) and falcarindiol (2) respectively (p < 0.05). Subsequent SV processing did not result in additional significant losses in polyacetylenes compared to blanched samples. Subsequent anaerobic storage of SV processed samples resulted in a significant decrease in 1 levels (p < 0.05) although no change in 2 levels was observed (p > 0.05). 1 levels in WI processed samples were significantly higher than in SV samples (p = 0.05). 2 was particularly susceptible to aerobic storage following WI processing with losses of up to 70% occurring after 5 days storage. 1 type polyacetylene undergoes degradation such as oxidation, dehydrogenation when thermally treated forming oxidized form of 1 type molecules, in this case falcarindione, dehydrofalcarinol, dehydrofalcarinone. Thermal processing had a significant effect on instrumental color of parsnip samples compared to minimally processed in both SV and WI processed samples resulting in parsnip disks becoming darker, yellower and browner following processing and storage.

An efficient particle filter for color-based tracking in complex scenes

In this paper, we introduce an efficient method for particle selection in tracking objects in complex scenes. Firstly, we improve the proposal distribution function of the tracking algorithm, including current observation, reducing the cost of evaluating particles with a very low likelihood. In addition, we use a partitioned sampling approach to decompose the dynamic state in several stages. It enables to deal with high-dimensional states without an excessive computational cost. To represent the color distribution, the appearance of the tracked object is modelled by sampled pixels. Based on this representation, the probability of any observation is estimated using non-parametric techniques in color space. As a result, we obtain a Probability color Density Image (PDI) where each pixel points its membership to the target color model. In this way, the evaluation of all particles is accelerated by computing the likelihood p(z|x) using the Integral Image of the PDI.