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Exploring Omics and AI in Food Quality Assessment: A Focus on Hazelnuts

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This thesis explores omics analytical strategies for assessing food quality, focusing on hazelnuts. It covers various analytical workflows, machine learning applications, and the role of artificial intelligence in food omics, providing insights into quality markers and methodologies for large-scale production.
  • main points
  • unique insights
  • practical applications
  • key topics
  • key insights
  • learning outcomes

  • • main points

    • 1
      Comprehensive coverage of omics workflows and their application in food quality assessment.
    • 2
      Integration of artificial intelligence techniques in food analysis, enhancing data processing and decision-making.
    • 3
      In-depth exploration of specific case studies related to hazelnuts, providing practical insights.

  • • unique insights

    • 1
      The application of comprehensive two-dimensional chromatography combined with AI for enhanced food analysis.
    • 2
      Innovative methodologies for quantifying volatile compounds and their implications for food quality.

  • • practical applications

    • The article provides valuable methodologies and insights for food scientists and industry professionals aiming to improve food quality assessment through advanced analytical techniques.

  • • key topics

    • 1
      Omics workflows in food quality assessment
    • 2
      Artificial Intelligence applications in food analysis
    • 3
      Quantitative analysis of volatile compounds in hazelnuts

  • • key insights

    • 1
      Integration of AI with traditional analytical methods for improved food quality assessment.
    • 2
      Detailed examination of the hazelnut volatilome and its implications for quality.
    • 3
      Innovative approaches to data fusion in omics studies.

  • • learning outcomes

    • 1
      Understand the role of omics in food quality assessment.
    • 2
      Apply AI techniques to enhance food analysis methodologies.
    • 3
      Evaluate the impact of analytical strategies on food quality in large-scale production.
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“ Introduction to Omics in Food Quality

Food quality assessment involves various parameters, including regulatory standards, sensory evaluation, and quality markers throughout the production chain. This section delves into the methodologies and importance of quality assessment in the food industry.

“ The Role of Omics in Food Analysis

Artificial intelligence is transforming food analysis by enhancing data processing and interpretation. This section explores how AI techniques, such as machine learning and data fusion, are applied in food omics to improve quality prediction and decision-making.

“ Case Study: Hazelnuts and Their Quality

The future of food quality assessment lies in the integration of advanced analytical techniques and AI. This section speculates on future trends and innovations that could further enhance the understanding and evaluation of food quality.

 Original link: https://iris.unito.it/retrieve/67e6cccb-6cf8-4d71-869b-6b979e04146b/PhD%20Thesis%20-%20Squara.pdf

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