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Related Concept Videos

Quantitative Analysis01:12

Quantitative Analysis

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Quantitative analysis is a technique for measuring the amount of specific constituents in a sample. When the sample's composition is unknown, qualitative analysis is performed first to identify its components, which ensures that the correct substances are measured during the quantitative phase.
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Instrument calibration is essential for ensuring that instruments produce accurate and consistent results. It is vital in manufacturing, healthcare, testing laboratories, and scientific research. Calibration processes are specific to each instrument and help enhance data accuracy. Each instrument has a unique calibration process tailored to its design and function to improve data accuracy.
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Digital image-based chemometrics for food analysis: a practical tutorial and roadmap.

Adriano de Araújo Gomes1, Paulo Henrique Gonçalves Dias Diniz2, David Douglas de Sousa Fernandes3

  • 1Universidade Federal do Rio Grande do Sul, Instituto de Química, Zip Code 90650-001, Porto Alegre, RS, Brazil; Institute of Analytical Chemistry, Faculty of Chemical and Food Technology, Slovak University of Technology in Bratislava, Radlinského 9, 812 37 Bratislava, Slovakia.

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Digital imaging and chemometrics offer powerful analytical methods for food quality control and safety. Standardization is crucial for reliable results, but recent advances like deep learning show promise for broader applications.

Keywords:
AuthenticationChemometricsClassificationDigital imagesFoodFraud

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Area of Science:

  • Food Science
  • Analytical Chemistry
  • Chemometrics

Background:

  • Digital imaging is increasingly used for food quality control, extracting chemical information beyond conventional signals.
  • Chemometric techniques significantly enhance the utility of image data in food analysis.
  • Existing literature shows diverse applications but lacks methodological standardization.

Purpose of the Study:

  • To review the applications of digital imaging in food analysis.
  • To provide a roadmap from univariate to multivariate approaches.
  • To highlight challenges and recent advancements in the field.

Main Methods:

  • Synthesis of literature on digital imaging applications in food analysis.
  • Illustration of methods through three case studies.
  • Discussion of univariate and multivariate chemometric techniques.
  • Review of recent advancements like deep learning and hybrid color descriptors.

Main Results:

  • Digital imaging, coupled with chemometrics, demonstrates significant potential for food safety and quality assessment.
  • Case studies illustrate the practical application of these techniques.
  • Recent advances improve the robustness and applicability of digital imaging in food science.

Conclusions:

  • Digital imaging offers a versatile platform for food analysis, complementing traditional methods.
  • Methodological standardization is essential for ensuring reliability and reproducibility in the field.
  • Emerging techniques like deep learning are poised to enhance the capabilities of digital imaging in food science.