On the Long Lasting “C‐Type” Structures in the Sodium Lidargram: The Lifetime of Kelvin‐Helmholtz Billows in the Mesosphere and Lower Thermosphere Region

In order to understand the characteristics of long‐lasting “C‐type” structure in the Sodium (Na) lidargram, six cases from different observational locations have been analyzed. The Na lidargram, collected from low‐, middle‐, and high‐latitude sites, show long lifetime of the C‐type structures which is believed to be the manifestation of Kelvin‐Helmholtz (KH) billows in the Mesosphere and Lower Thermosphere (MLT) region. In order to explore the characteristics of the long‐lasting C‐type structures, the altitude profile of square of Brunt‐Väisälä frequency in the MLT region has been derived using the temperature profile collected from the Na lidar instruments and the SABER instrument onboard TIMED satellite. It is found to be positive in the C‐type structure region for all the six cases which indicates that the regions are convectively stable. Simultaneous wind measurements, which allowed us to calculate the Richardson numbers and Reynolds numbers for three cases, suggest that the regions where the C‐type structure appeared were dynamically stable and nonturbulent. This paper brings out a hypothesis wherein the low temperature can increase the magnitude of the Prandtl number and convectively stable atmospheric region can cause the magnitude of Reynolds number to decrease. As a consequence, the remnant of previously generated KH billows in nearly “frozen‐in” condition can be advected through this conducive region to a different location by the background wind where they can sustain for a long time without much deformation. These long‐lived KH billows in the MLT region will eventually manifest the long‐lasting C‐type structures in the Na lidargram

Flavor Profiling Using Comprehensive Mass Spectrometry Analysis of Metabolites in Tomato Soups

Trained sensory panels are regularly used to rate food products but do not allow for data-driven approaches to steer food product development. This study evaluated the potential of a molecular-based strategy by analyzing 27 tomato soups that were enhanced with yeast-derived flavor products using a sensory panel as well as LC-MS and GC-MS profiling. These data sets were used to build prediction models for 26 different sensory attributes using partial least squares analysis. We found driving separation factors between the tomato soups and metabolites predicting different flavors. Many metabolites were putatively identified as dipeptides and sulfur-containing modified amino acids, which are scientifically described as related to umami or having “garlic-like” and “onion-like” attributes. Proposed identities of high-impact sensory markers (methionyl-proline and asparagine-leucine) were verified using MS/MS. The overall results highlighted the strength of combining sensory data and metabolomics platforms to find new information related to flavor perception in a complex food matrix

Flavor Profiling Using Comprehensive Mass Spectrometry Analysis of Metabolites in Tomato Soups

Trained sensory panels are regularly used to rate food products but do not allow for data-driven approaches to steer food product development. This study evaluated the potential of a molecular-based strategy by analyzing 27 tomato soups that were enhanced with yeast-derived flavor products using a sensory panel as well as LC-MS and GC-MS profiling. These data sets were used to build prediction models for 26 different sensory attributes using partial least squares analysis. We found driving separation factors between the tomato soups and metabolites predicting different flavors. Many metabolites were putatively identified as dipeptides and sulfur-containing modified amino acids, which are scientifically described as related to umami or having “garlic-like” and “onion-like” attributes. Proposed identities of high-impact sensory markers (methionyl-proline and asparagine-leucine) were verified using MS/MS. The overall results highlighted the strength of combining sensory data and metabolomics platforms to find new information related to flavor perception in a complex food matrix

Flavor Profiling Using Comprehensive Mass Spectrometry Analysis of Metabolites in Tomato Soups

Trained sensory panels are regularly used to rate food products but do not allow for data-driven approaches to steer food product development. This study evaluated the potential of a molecular-based strategy by analyzing 27 tomato soups that were enhanced with yeast-derived flavor products using a sensory panel as well as LC-MS and GC-MS profiling. These data sets were used to build prediction models for 26 different sensory attributes using partial least squares analysis. We found driving separation factors between the tomato soups and metabolites predicting different flavors. Many metabolites were putatively identified as dipeptides and sulfur-containing modified amino acids, which are scientifically described as related to umami or having “garlic-like” and “onion-like” attributes. Proposed identities of high-impact sensory markers (methionyl-proline and asparagine-leucine) were verified using MS/MS. The overall results highlighted the strength of combining sensory data and metabolomics platforms to find new information related to flavor perception in a complex food matrix