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Tomato Analyzer: A Useful Software Application to Collect Accurate and Detailed Morphological and Colorimetric Data from Two-dimensional Objects
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Tomato classification using mass spectrometry-machine learning technique: A food safety-enhancing platform.

Arthur Noin de Oliveira1, Sophia Regina Frazatto Bolognini1, Luiz Claudio Navarro1

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Machine learning accurately classifies organic and non-organic tomatoes using mass spectrometry data. This food safety tool achieved 92% accuracy, aiding quality assessment for consumers and industry.

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Area of Science:

  • Agricultural Science
  • Analytical Chemistry
  • Food Science

Background:

  • Food safety and quality assessment are critical global challenges.
  • Distinguishing organic from non-organic produce is essential for consumers and regulatory bodies.
  • Existing methods for produce classification can be time-consuming and lack precision.

Purpose of the Study:

  • To develop a Machine Learning (ML) platform for classifying tomatoes as organic or non-organic.
  • To leverage Mass Spectrometry (MS) data for automated produce analysis.
  • To enhance food safety and quality assessment mechanisms through advanced data analytics.

Main Methods:

  • Tomato samples were analyzed using direct-infusion electrospray-ionization mass spectrometry (DI-ESI-MS) coupled with silica gel plates.
  • A Decision Tree algorithm was employed for data analysis and classification.
  • The ML model was trained and validated on the generated MS data.

Main Results:

  • The Decision Tree model achieved high classification performance: 92% accuracy, 94% sensitivity, and 90% precision.
  • The analysis identified potential biomarkers indicative of differences in organic and non-organic tomato production.
  • The platform demonstrated feasibility for rapid and reliable classification of tomato origin.

Conclusions:

  • Machine learning analysis of MS data provides an effective method for classifying organic and non-organic tomatoes.
  • This approach can significantly improve food safety and quality control in the agricultural industry.
  • The identified biomarkers offer insights into production practices and can be further investigated.