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The sense of smell is achieved through the activities of the olfactory system. It starts when an airborne odorant enters the nasal cavity and reaches olfactory epithelium (OE). The OE is protected by a thin layer of mucus, which also serves the purpose of dissolving more complex compounds into simpler chemical odorants. The size of the OE and the density of sensory neurons varies among species; in humans, the OE is only about 9-10 cm2.
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Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
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Aromatic compounds can be identified or analyzed using proton NMR and carbon‐13 NMR. Typically, aromatic hydrogens or hydrogens directly bonded to the aromatic rings are strongly deshielded by the aromatic ring current. Therefore, they absorb in the range of 6.5–8.0 ppm in proton NMR spectra. For instance, aromatic hydrogens directly bonded to the benzene ring absorb at 7.3 ppm. However, aromatic hydrogens of larger rings absorb farther upfield or downfield than the ideal range.
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Titrimetric analysis in solution chemistry involves measuring the volume of solutions and is often called volumetric analysis. The standard solution of known concentration in the burette is called the titrant, whereas the solution of unknown concentration in the flask is called the analyte, or titrand. Titrimetric analyses can be classified into four types based on the reactions between the titrant and analyte.
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Related Experiment Video

Updated: May 22, 2025

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction
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Deep Learning for Odor Prediction on Aroma-Chemical Blends.

Laura Sisson1, Aryan Amit Barsainyan2, Mrityunjay Sharma3,4,5

  • 1Boston University, Boston, Massachusetts, 02215, United States.

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Summary

Deep learning models now predict aroma chemical blend qualities better than humans. This research introduces graph neural networks for predicting scent profiles of multiple aroma molecules.

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Area of Science:

  • Computational chemistry
  • Chemosensation
  • Machine learning in chemical sciences

Background:

  • Deep learning models excel at predicting olfactory qualities of individual aroma chemicals.
  • Industry applications often require predicting scent profiles of complex aroma chemical blends, a challenge not fully addressed by public research.

Purpose of the Study:

  • To develop and evaluate deep learning models for predicting olfactory qualities of aroma chemical blends.
  • To explore established and novel approaches using a curated dataset of molecule pairs.

Main Methods:

  • Compilation of a novel dataset containing labeled pairs of aroma chemicals.
  • Application of established and novel graph neural network (GNN) architectures.
  • Analysis of how GNN model architecture variations impact predictive performance for blend olfactory qualities.

Main Results:

  • Developed GNN models that accurately predict olfactory qualities of aroma chemical blends.
  • Demonstrated that model architecture significantly influences predictive accuracy for blend scent profiles.
  • Achieved predictive performance surpassing previous benchmarks for blend olfaction.

Conclusions:

  • Graph neural networks are effective tools for predicting the emergent olfactory properties of aroma chemical mixtures.
  • Model architecture optimization is crucial for maximizing predictive performance in blend olfaction.
  • This work bridges the gap between academic research and industrial needs in aroma science.