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

Mass Spectrometry: Complex Analysis01:21

Mass Spectrometry: Complex Analysis

712
Mass spectrometry is an important technique for the identification of pure compounds. However, it has some limitations for the analysis of complex mixtures, often due to excessive fragmentation making the spectrum too complicated to decipher. Mass spectrometry can be combined with suitable separation methods in sequence, forming hyphenated methods, which are useful in the analysis of complex mixtures.
GC–MS is a powerful hyphenated method commonly used in forensics and environmental...
712

You might also read

Related Articles

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

Sort by
Same author

Acrylamide Mitigation in Popcorn: A Comparison of Innovative Techniques.

Foods (Basel, Switzerland)·2026
Same author

Valorization of Food Industry By-Products for Sustainable Functional Food Production: Recent Advances and Future Perspectives.

Foods (Basel, Switzerland)·2026
Same author

Protective Effects of <i>Schinus terebinthifolius</i> Leaf Supercritical Fluid Extract Against UVC-Induced Oxidative Stress: A Com-Prehensive Gene Expression Study.

International journal of molecular sciences·2026
Same author

Bioactive Lipophilic Antioxidants (Carotenoids, Tocols, Retinol, and Coenzyme Q<sub>10</sub>) in Human and Animal Tissues: Development and Validation of a Rapid Extraction and Chromatographic Method for Nutrition and Health Studies.

Antioxidants (Basel, Switzerland)·2026
Same author

An Exploratory Study of the Nutritional Composition and Caco-2 Safety Assessment of Elche Date Flour and Its Green Hydroethanolic Extracts.

Foods (Basel, Switzerland)·2025
Same author

Phenolic Fingerprints of Spanish Olive Mill Wastewaters (Alpechin): A Step Toward Regional Valorization Through Antioxidant Recovery.

Antioxidants (Basel, Switzerland)·2025

Related Experiment Video

Updated: Jun 3, 2025

Multi-step Preparation Technique to Recover Multiple Metabolite Compound Classes for In-depth and Informative Metabolomic Analysis
11:25

Multi-step Preparation Technique to Recover Multiple Metabolite Compound Classes for In-depth and Informative Metabolomic Analysis

Published on: July 11, 2014

33.5K

Optimizing Recovery of High-Added-Value Compounds from Complex Food Matrices Using Multivariate Methods.

Yixuan Liu1, Basharat N Dar2, Hilal A Makroo2

  • 1Research Group in Innovative Technologies for Sustainable Food (ALISOST), Preventive Medicine and Public Health, Food Science, Toxicology and Forensic Medicine Department, Faculty of Pharmacy, Universitat de València, Avda. Vicent Andrés Estellés, s/n, 46100 Burjassot, Spain.

Antioxidants (Basel, Switzerland)
|January 8, 2025
PubMed
Summary
This summary is machine-generated.

Optimizing food processing with Response Surface Methodology (RSM) and Artificial Neural Networks (ANNs) enhances compound recovery and sustainability. These methods reduce waste and improve product quality for a greener food industry.

Keywords:
ANNRSMfood industryhigh-added-value compoundsoptimization

More Related Videos

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

12.3K
Detection of Regulated Ergot Alkaloids in Food Matrices by Liquid Chromatography-Trapped Ion Mobility Spectrometry-Time-of-Flight Mass Spectrometry
08:56

Detection of Regulated Ergot Alkaloids in Food Matrices by Liquid Chromatography-Trapped Ion Mobility Spectrometry-Time-of-Flight Mass Spectrometry

Published on: November 22, 2024

556

Related Experiment Videos

Last Updated: Jun 3, 2025

Multi-step Preparation Technique to Recover Multiple Metabolite Compound Classes for In-depth and Informative Metabolomic Analysis
11:25

Multi-step Preparation Technique to Recover Multiple Metabolite Compound Classes for In-depth and Informative Metabolomic Analysis

Published on: July 11, 2014

33.5K
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

12.3K
Detection of Regulated Ergot Alkaloids in Food Matrices by Liquid Chromatography-Trapped Ion Mobility Spectrometry-Time-of-Flight Mass Spectrometry
08:56

Detection of Regulated Ergot Alkaloids in Food Matrices by Liquid Chromatography-Trapped Ion Mobility Spectrometry-Time-of-Flight Mass Spectrometry

Published on: November 22, 2024

556

Area of Science:

  • Food Science and Technology
  • Process Optimization
  • Sustainable Manufacturing

Background:

  • Optimizing the recovery of high-value compounds is essential for improving quality and yield in the food industry.
  • Multivariate statistical methods are critical tools for achieving these optimizations.
  • Sustainability in food processing necessitates efficient resource utilization and waste reduction.

Purpose of the Study:

  • To compare the technical strengths of Response Surface Methodology (RSM) and Artificial Neural Networks (ANNs).
  • To examine the sustainability impacts of RSM and ANNs in food processing.
  • To highlight the potential of these methods in driving innovation and greener practices.

Main Methods:

  • Review and comparison of Response Surface Methodology (RSM) for structured process modeling.
  • Review and comparison of Artificial Neural Networks (ANNs) for handling nonlinear data and large datasets.
  • Analysis of synergistic approaches combining RSM and ANNs for enhanced predictive modeling.

Main Results:

  • RSM offers a structured approach to modeling complex food processing systems.
  • ANNs demonstrate proficiency in managing nonlinear relationships and extensive datasets.
  • Combined RSM and ANNs provide synergistic benefits for predictive accuracy, nutrient preservation, and shelf-life extension.

Conclusions:

  • RSM and ANNs are powerful tools for optimizing resource use and minimizing waste in the food industry.
  • The integration of RSM and ANNs supports sustainable food processing and enhances product quality.
  • Further research is needed to explore the scalability and integration of these methods with emerging technologies for broader industry application.