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

Classification of Titrimetric Analysis Based on Reaction Types01:01

Classification of Titrimetric Analysis Based on Reaction Types

2.0K
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.
Titrations between an acid and a base lead to neutralization reactions that form...
2.0K
Volatilization01:10

Volatilization

6.4K
Volatilization gravimetry is an analytical technique that measures the mass lost due to the volatilization of the substance. This technique is used to estimate the amount of volatile material in a sample. To perform this method, heat a known amount of the sample to a high temperature in a crucible or other suitable vessel. The volatile substance in the sample evaporates, and the vapor is completely expelled from the crucible either by heating the sample or bubbling a stream of inert gas through...
6.4K
Chromatographic Methods: Classification01:12

Chromatographic Methods: Classification

4.6K
Chromatographic techniques are classified in three ways: the classification is based on the physical state of the stationary and mobile phases, how the mobile phase and the stationary phase contact each other, or through the chemical or physical processes that isolate the components of the sample. Typically, the mobile phase is either a liquid or gas, while the stationary phase is either a solid or a liquid layer applied to a solid surface.
Chromatographic techniques are typically named by...
4.6K
Distillation: Vapor–Liquid Equilibria01:01

Distillation: Vapor–Liquid Equilibria

5.6K
Distillation is a separation technique that takes advantage of the boiling point properties of disparate elements in a mixture. To perform distillation, we begin by heating a miscible mixture of two liquids with a significant difference in boiling points (at least 20°C). As the solution heats up and reaches the bubble point of the more volatile component, some molecules of the more volatile component transition into the gas phase and travel upward into the condenser, which is a glass tube...
5.6K
Classification of Elements and Compounds02:54

Classification of Elements and Compounds

77.1K
Pure substances consist of only one type of matter. A pure substance can be an element or a compound. An element consists of only one type of atom, while a compound consists of two or more types of atoms held together by a chemical bond. Elements are classified as atomic or molecular based on the nature of their basic units.
Compounds are pure substances composed of two or more elements in fixed, definite proportions. Compounds are classified as ionic or molecular (covalent) based on the bonds...
77.1K
Classification of Systems-I01:26

Classification of Systems-I

673
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
673

You might also read

Related Articles

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

Sort by
Same author

Exogenous salicylic acid promotes the accumulation of volatile sulfur compounds and anti-muscle atrophy activity in garlic sprouts.

Journal of the science of food and agriculture·2026
Same author

Histology-specific ADC target landscapes in ovarian cancer and therapy-associated antigen downshift after ADC exposure.

Gynecologic oncology·2026
Same author

Chemical fingerprinting of Korean ginseng (<i>Panax ginseng</i>) and American ginseng (<i>Panax quinquefolius</i>) using multi-platform metabolomics and taste profiling.

Food science and biotechnology·2026
Same author

Optimizing brown rice liquefaction and saccharification using response surface methodology for grain ethanol production.

Scientific reports·2026
Same author

Methyl hexenoate and linalool emitted by hot peppers repel western flower thrips (Frankliniella occidentalis) and attract their predator (Orius laevigatus).

Scientific reports·2025
Same author

Stromal secreted protein acidic and rich in cysteine expression: A potential target for improved prognosis in patients with pancreatic cancer.

World journal of gastrointestinal oncology·2025

Related Experiment Video

Updated: Apr 5, 2026

Fruit Volatile Analysis Using an Electronic Nose
11:02

Fruit Volatile Analysis Using an Electronic Nose

Published on: March 30, 2012

22.6K

Classification of vinegar types using volatile compound profiles and machine learning.

Sunhyun Park1, Keono Kim2, Jeehye Sung2

  • 1Food Standard Research Center, Korea Food Research Institute, 245 Nongsaengmyeong-Ro, Wanju 55365, Republic of Korea.

Food Chemistry
|April 3, 2026
PubMed
Summary

This study analyzed volatile compounds in four vinegar types using advanced analytical techniques and machine learning. The Random Forest model accurately classified vinegar types based on their unique aroma profiles.

Keywords:
Headspace solid-phase microextraction coupled with gas chromatography–mass spectrometry (HS-SPME/GC–MS)Machine learningRandom Forest with recursive feature elimination and cross-validation (RF-RFECV)Shapley additive exPlanations (SHAP)VinegarVolatile compounds

More Related Videos

Profiling Volatile Compounds in Blackcurrant Fruit using Headspace Solid-Phase Microextraction Coupled to Gas Chromatography-Mass Spectrometry
05:29

Profiling Volatile Compounds in Blackcurrant Fruit using Headspace Solid-Phase Microextraction Coupled to Gas Chromatography-Mass Spectrometry

Published on: June 9, 2021

4.7K
PTR-ToF-MS Coupled with an Automated Sampling System and Tailored Data Analysis for Food Studies: Bioprocess Monitoring, Screening and Nose-space Analysis
08:43

PTR-ToF-MS Coupled with an Automated Sampling System and Tailored Data Analysis for Food Studies: Bioprocess Monitoring, Screening and Nose-space Analysis

Published on: May 11, 2017

13.1K

Related Experiment Videos

Last Updated: Apr 5, 2026

Fruit Volatile Analysis Using an Electronic Nose
11:02

Fruit Volatile Analysis Using an Electronic Nose

Published on: March 30, 2012

22.6K
Profiling Volatile Compounds in Blackcurrant Fruit using Headspace Solid-Phase Microextraction Coupled to Gas Chromatography-Mass Spectrometry
05:29

Profiling Volatile Compounds in Blackcurrant Fruit using Headspace Solid-Phase Microextraction Coupled to Gas Chromatography-Mass Spectrometry

Published on: June 9, 2021

4.7K
PTR-ToF-MS Coupled with an Automated Sampling System and Tailored Data Analysis for Food Studies: Bioprocess Monitoring, Screening and Nose-space Analysis
08:43

PTR-ToF-MS Coupled with an Automated Sampling System and Tailored Data Analysis for Food Studies: Bioprocess Monitoring, Screening and Nose-space Analysis

Published on: May 11, 2017

13.1K

Area of Science:

  • Food Science
  • Analytical Chemistry
  • Biotechnology

Background:

  • Vinegar's aroma diversity stems from volatile metabolites produced during fermentation.
  • Limited research exists on comprehensive volatile profiles across different vinegar types.
  • Understanding these profiles is crucial for quality control and authenticity.

Purpose of the Study:

  • To characterize volatile profiles of rice, grape, apple, and persimmon vinegars.
  • To develop a robust, data-driven method for vinegar classification.
  • To identify key volatile compounds specific to each vinegar type.

Main Methods:

  • Headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry (HS-SPME/GC-MS) was employed.
  • Machine learning models, particularly Random Forest, were utilized for classification.
  • Shapley Additive exPlanations (SHAP) were used to interpret feature importance.

Main Results:

  • 127 volatile compounds were identified across 87 commercial vinegar samples.
  • The Random Forest model achieved high accuracy (96.19%) in classifying vinegar types.
  • Specific volatile compounds like methyl acetate, acetic acid, and benzaldehyde were identified as key discriminators for persimmon, grape, and rice vinegars, respectively.

Conclusions:

  • A robust, data-driven framework for vinegar classification using volatile profiles was established.
  • The study identified fermentation-derived volatiles and potential contaminants.
  • This approach provides a foundation for future validation studies in vinegar authentication and quality assessment.