Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Olfaction01:25

Olfaction

49.8K
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.
The olfactory receptors are embedded in the cilia of the...
49.8K
Physiology of Smell and Olfactory Pathway01:20

Physiology of Smell and Olfactory Pathway

14.1K
Humans detect odors with the help of specialized cells located in the upper part of the nasal cavity, called olfactory receptor neurons (ORNs). ORNs possess hair-like structures called cilia, which are receptive to sensations from the inhaled air. When an odorant molecule binds to a specific receptor on the cell of the cilia, it leads to a series of events that ultimately cause the ORN to send electrical signals to the olfactory bulb in the brain through the olfactory nerves.
The olfactory...
14.1K
Predicting Products: SN1 vs. SN202:27

Predicting Products: SN1 vs. SN2

17.7K
Nucleophilic substitution reactions of alkyl halides can proceed via an SN1 or an SN2 mechanism. While in SN2 reactions, the nucleophile attacks the substrate simultaneously as the leaving group departs, in SN1 reactions, the substrate first dissociates to give the carbocation intermediate. Various factors such as the structure of the substrate, the strength of the nucleophile, and the nature of the solvent promote one mechanism over the other.
With increased substitution on the alkyl halide,...
17.7K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

CD23 sustains NKT17 cell homeostasis via IL-7Rα-mTORC2 signaling.

Cell death & disease·2026
Same author

Childhood Ayme-Gripp syndrome: A case report.

The Journal of international medical research·2026
Same author

Hydroxypropylation product of amaranth starch showing high freeze-thaw stability.

Food research international (Ottawa, Ont.)·2026
Same author

Intra-articular injection of Erastin induces OA pathological progression as an experimental model.

International immunopharmacology·2026
Same author

Bisphenol A-mediated root exudates of ryegrass as potential activators of functional succession in the rhizosphere microorganisms: Mechanistic insights into microbial community assembly and biodegradation.

Environmental research·2026
Same author

Corrigendum to "Carboxymethyl chitosan incorporated with gliadin/phlorotannin nanoparticles enables the formation of new active packaging films" [Int. J. Biol. Macromol. 203 (2022) 40-48].

International journal of biological macromolecules·2026
Same journal

Unravelling the multi-scale structural organisation of in vivo ileal digesta from diets containing protein-seaweed polysaccharide blends.

Food research international (Ottawa, Ont.)·2026
Same journal

pH and temperature-dependent gelation of Torreya grandis nut protein: Molecular interactions and gel network formation.

Food research international (Ottawa, Ont.)·2026
Same journal

Sublethal cinnamon essential oil induces oxidative stress-driven metabolic reprogramming and attenuates spoilage potential in Psychrobacter faecalis.

Food research international (Ottawa, Ont.)·2026
Same journal

Modeling the effect of temperature and macronutrient composition on water activity and the influence on thermal resistance of Salmonella.

Food research international (Ottawa, Ont.)·2026
Same journal

Valorization of coffee Silverskin into a novel dietary Fiber ingredient: A comprehensive study on structure, and in vitro/in vivo antioxidant activity.

Food research international (Ottawa, Ont.)·2026
Same journal

Effect of cold plasma treatment on the allergenicity of hazelnut proteins in a BALB/c mouse model.

Food research international (Ottawa, Ont.)·2026
See all related articles

Related Experiment Video

Updated: Mar 29, 2026

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction
06:19

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction

Published on: August 16, 2024

941

Predicting soymilk odors using a multilayer perceptron neural network model.

Yuhang Liu1, Xingyun Peng1, Zhimin Li1

  • 1Beijing Key Laboratory of Plant Protein and Cereal Processing, College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, China.

Food Research International (Ottawa, Ont.)
|March 27, 2026
PubMed
Summary
This summary is machine-generated.

Scientists developed a neural network to predict soymilk odor from volatile compounds. This technology helps identify soybean varieties (SV-SSO) with pleasant beany aromas, improving consumer acceptance of soymilk products.

Keywords:
Beany odorsMachine learning modelNeural network algorithmSoymilk volatiles

More Related Videos

O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression
06:50

O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression

Published on: November 8, 2019

7.1K
Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements
10:25

Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements

Published on: June 28, 2016

11.3K

Related Experiment Videos

Last Updated: Mar 29, 2026

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction
06:19

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction

Published on: August 16, 2024

941
O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression
06:50

O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression

Published on: November 8, 2019

7.1K
Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements
10:25

Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements

Published on: June 28, 2016

11.3K

Area of Science:

  • Food Science and Technology
  • Sensory Analysis
  • Computational Chemistry

Background:

  • Undesirable beany odors in soymilk reduce consumer acceptance, posing a challenge for manufacturers.
  • Identifying soybean varieties with desirable odors (SV-SSO) is crucial for product improvement.
  • Automatic odor prediction from volatile profiles can accelerate the discovery of SV-SSO.

Purpose of the Study:

  • To establish a database correlating soymilk key volatile (SKV) content with sensory odor scores.
  • To develop and validate a predictive model for soymilk odor based on volatile profiles.
  • To elucidate the contribution of individual SKVs to soymilk aroma and overall consumer satisfaction.

Main Methods:

  • Compilation of a comprehensive database of soymilk key volatile (SKV) compounds and associated sensory data.
  • Development and training of a multilayer perceptron (MLP) neural network model using the established database.
  • Analysis of MLP weight matrices to determine the mathematical contribution of SKVs to odor perception.

Main Results:

  • The MLP model accurately predicted soymilk odor with an average relative error of ≤5%.
  • Specific volatile compounds including hexanal, hexanol, E-2-hexenal, and 1-octen-3-ol were identified as key contributors to pleasant beany odor.
  • All analyzed SKVs were found to mathematically influence unpleasant beany odor and overall consumer satisfaction.

Conclusions:

  • The study provides mathematical insights into the role of soymilk headspace SKVs in odor formation.
  • The developed predictive model offers a technical pathway for identifying and developing soymilk with improved sensory attributes.
  • Findings support the development of better-tasting soymilk products through targeted selection of soybean varieties and aroma compound management.