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Related Concept Videos

Variability: Analysis01:11

Variability: Analysis

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Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
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Tandem Mass Spectrometry01:21

Tandem Mass Spectrometry

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Tandem mass spectrometry is a technique that uses multiple mass analyzers in series to obtain a higher selectivity and signal-to-noise ratio for the analyte. Instruments with multiple analyzers separated by an interaction cell enable secondary fragmentation and selected study of the fragment ions.
Secondary fragmentations occur in the interaction cell and can be induced by various factors. Fragmentation induced by collision with inert gases, such as N2, Ar, He, etc., is called collision-induced...
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Genetic Variation01:25

Genetic Variation

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Genetic variation is the diversity in DNA sequences found among individuals of the same species. This diversity is crucial for a species' survival because it helps organisms adapt to environmental changes. Genetic variation begins with fertilization, where an egg and sperm cell merge. Each of these cells carries 23 chromosomes, up to 46 in the fertilized egg. Chromosomes are long DNA strands that contain genes, the basic units of heredity.
Genes exist in different versions called alleles,...
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High-Resolution Mass Spectrometry (HRMS)01:15

High-Resolution Mass Spectrometry (HRMS)

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The resolution of a mass spectrometer depends on the efficiency of separating ions with different ion masses. The mass of an atom is approximated to the sum of the masses of protons and neutrons inside, considering the masses of protons and neutrons as equal. However, the masses of the proton (1.6726 × 10−24 g) and neutron (1.6749 × 10−24 g) are not truly equal. There is a minor error in the expression of atomic masses relative to the simplest atom of hydrogen. For...
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Volatilization01:10

Volatilization

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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...
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Extraction: Partition and Distribution Coefficients01:14

Extraction: Partition and Distribution Coefficients

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The distribution law or Nernst's distribution law is the law that governs the distribution of a solute between two immiscible solvents. This law, also known as the partition law, states that if a solute is added to the mixture of two immiscible solvents at a constant temperature, the solute is distributed between the two solvents in such a way that the ratio of solute concentrations in the solvents remains constant at equilibrium.
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PTR-ToF-MS Coupled with an Automated Sampling System and Tailored Data Analysis for Food Studies: Bioprocess Monitoring, Screening and Nose-space Analysis
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Disentangling shared and unique variation in multiplatform hazelnut volatilomics using JIVE.

Maria Mazzucotelli1, Iuliia Khomenko2, Emanuela Betta2

  • 1Research and Innovation Centre, Fondazione Edmund Mach, San Michele all'Adige, Trento, Italy; C3A - Center Agriculture Food Environment, University of Trento, San Michele all'Adige, Trento, Italy.

Talanta
|February 21, 2025
PubMed
Summary
This summary is machine-generated.

Integrating data from multiple analytical techniques like GC-MS, PTR-ToF-MS, and GC-IMS is challenging. The Joint and Individual Variation Explained (JIVE) method effectively separates shared and unique information from volatilome data, aiding in food quality analysis.

Keywords:
GC-IMSGC-MSPTR-ToF-MSRoastingVOCsVolatilome

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Area of Science:

  • Food Science and Technology
  • Analytical Chemistry
  • Chemometrics

Background:

  • Volatile metabolites are key indicators of food sensory quality, acceptability, and traceability.
  • Comprehensive volatilome characterization typically necessitates the integration of multiple analytical techniques.
  • Integrating data from independent analytical platforms to identify shared and unique information presents a significant challenge.

Purpose of the Study:

  • To demonstrate the application of the multivariate Joint and Individual Variation Explained (JIVE) approach for integrating multiplatform volatilome data.
  • To analyze the volatilome of hazelnut pastes using Gas Chromatography-Mass Spectrometry (GC-MS), Proton Transfer Reaction-Time of Flight-Mass Spectrometry (PTR-ToF-MS), and Gas Chromatography-Ion Mobility Spectrometry (GC-IMS).
  • To evaluate the complementarity of different analytical techniques and guide the selection of optimal methods.

Main Methods:

  • Characterization of hazelnut paste volatilome using GC-MS, PTR-ToF-MS, and GC-IMS.
  • Development of an automated pipeline for untargeted peak table extraction from GC-IMS data.
  • Application of the Joint and Individual Variation Explained (JIVE) multivariate approach for data integration and analysis.

Main Results:

  • JIVE successfully decomposed the variability within each dataset into joint and individual components.
  • A high-level comparison confirmed the complementarity of GC-MS, PTR-ToF-MS, and GC-IMS based on variation decomposition and variable distribution.
  • Latent variables derived from JIVE facilitated visualization of shared and platform-specific analytical patterns and identification of key variable trends.

Conclusions:

  • The JIVE approach offers a robust strategy for the unsupervised exploration and interpretation of multiplatform volatilome data.
  • JIVE provides clearer insights into both shared and technique-specific information, supporting objective evaluation of multiplatform analysis.
  • This method aids in understanding the potential of combined analytical techniques and guides the selection of suitable methods for specific research questions.