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Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
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Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
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Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Class-modeling approach to PTR-TOFMS data: a peppers case study.

Cosimo Taiti1, Corrado Costa, Paolo Menesatti

  • 1Università degli Studi di Firenze, Dipartimento di Scienze delle Produzioni Agroalimentari e dell'Ambiente, 50019, Sesto Fiorentino (FI), Italy.

Journal of the Science of Food and Agriculture
|May 30, 2014
PubMed
Summary
This summary is machine-generated.

Proton transfer reaction-time-of-flight mass spectrometry rapidly identified volatile compounds in chili peppers. Advanced analysis successfully distinguished between Capsicum species, highlighting key compounds like dimethyl sulfide, hexanal, and benzaldehyde.

Keywords:
PLS-DAVIP scoreschili pepper (Capsicum spp.)class modelingproton transfer reaction-mass spectrometryvolatile organic compounds (VOC)

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Area of Science:

  • Analytical Chemistry
  • Plant Science
  • Food Science

Background:

  • Proton transfer reaction-mass spectrometry (PTR-MS) is a rapid technique for analyzing volatile compounds.
  • A recent implementation, PTR-TOFMS, enhances speed and accuracy in volatile compound determination.
  • Fruits of Capsicum spp. (chili peppers) possess a complex volatile profile.

Purpose of the Study:

  • To analyze the volatile organic compounds (VOCs) emission profile of freshly cut chili peppers.
  • To differentiate between three species and 33 cultivars of Capsicum using PTR-TOFMS.
  • To identify key volatile compounds responsible for species discrimination.

Main Methods:

  • Proton transfer reaction-time-of-flight mass spectrometry (PTR-TOFMS) was employed for volatile compound analysis.
  • Multivariate class-modeling approaches were utilized for data analysis.
  • Variable importance in projection (VIP) scores were calculated to identify significant compounds.

Main Results:

  • PTR-TOFMS data successfully discriminated among the three Capsicum species with 100% accuracy.
  • Fifteen key volatile compounds were identified as important for species discrimination.
  • Tentative identification of dimethyl sulfide (m/z 63.027), hexanal (m/z 101.096), and benzaldehyde (m/z 107.050) as important discriminators.

Conclusions:

  • PTR-TOFMS coupled with multivariate analysis is a powerful tool for characterizing Capsicum volatile profiles.
  • Dimethyl sulfide, hexanal, and benzaldehyde are significant volatile markers for Capsicum species differentiation.
  • Further exploration of advanced multivariate techniques beyond classification is warranted for volatile compound analysis.