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Related Concept Videos

Microbial Growth Measurement: Indirect Methods01:27

Microbial Growth Measurement: Indirect Methods

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Estimating microbial growth is essential for understanding population dynamics and environmental adaptations. Indirect methods provide valuable insights by measuring parameters such as turbidity, metabolic activity, and biomass, enabling efficient and reproducible assessments.During exponential growth, microbial cells scatter light proportionally to their biomass, a principle used in turbidity measurements. About one million cells per milliliter produce detectable scattering, which a...
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Geographic Information Systems (GIS) operate across three levels of application, each representing an increasing degree of complexity: data management, analysis, and prediction. These levels reflect the expanding functionality and versatility of GIS technology in handling spatial data for diverse purposes.Data ManagementAt its foundational level, GIS serves as a tool for data management, enabling the input, storage, retrieval, and organization of spatial data. This level is often employed in...
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Direct methods for measuring microbial populations in a culture are essential tools in microbiology, providing quantitative data for various applications. Among these, microscopic counts, plate counts, and serial dilution are widely used techniques, each with unique principles and applications.Microscopic CountsMicroscopic counting involves the use of a Petroff-Hausser chamber, a specialized microscope slide with a grid and defined depth. By observing a liquid culture under a microscope,...
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Updated: Oct 10, 2025

Quantification of the Potential Impact of Glyphosate-Based Products on Microbiomes
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hGLUTEN Tool: Measuring Its Social Impact Indicators.

Antonia Moreno1, Guillermo Sanz1, Begonya Garcia-Zapirain1

  • 1eVIDA Research Group, University of Deusto, Avd. de las Universidades, 24, 48007 Bilbao, Spain.

International Journal of Environmental Research and Public Health
|December 10, 2021
PubMed
Summary
This summary is machine-generated.

The hGLUTEN tool for detecting gluten and spoilage shows high user engagement in research. Most users are unaware of similar technologies, indicating a significant market potential for this food safety innovation.

Keywords:
ODSartificial intelligenceembedded systemsensor NIRsocial impact

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Area of Science:

  • Food Science and Technology
  • Social Impact Assessment
  • Consumer Behavior

Background:

  • The hGLUTEN tool offers a technological solution for detecting gluten and spoiled food.
  • Assessing the social impact of new technologies is crucial for understanding their real-world value and adoption potential.

Purpose of the Study:

  • To measure the social impact of the hGLUTEN tool among professionals and end-users.
  • To evaluate user engagement and awareness of existing gluten-detection technologies.

Main Methods:

  • Utilized two Likert scale surveys targeting engineers, chefs, and general end-users.
  • Assessed data against European Commission's Horizon Europe Key Impact Pathways and SIOR criteria.

Main Results:

  • High relevance (85-100%) was found for participating in research projects across all surveyed groups.
  • A significant majority (88% of users, 5% of chefs) were unaware of alternative gluten-detection technologies.
  • Identified a need for user motivation tools and increased media visibility to enhance the tool's impact.

Conclusions:

  • The hGLUTEN tool demonstrates strong potential for user engagement and market penetration due to low awareness of competing technologies.
  • Future development should focus on enhancing user motivation and public visibility to maximize the tool's societal and commercial impact.