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Low-Cost Sensor for Lycopene Content Measurement in Tomato Based on Raspberry Pi 4.

Marcos-Jesús Villaseñor-Aguilar1,2, José-Alfredo Padilla-Medina2, Juan Prado-Olivarez2

  • 1Departamento de Ingeniería de Robótica y de Datos, Universidad Politécnica de Guanajuato, Cortazar 38496, Mexico.

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Summary
This summary is machine-generated.

This study introduces a low-cost sensor for measuring lycopene in tomatoes using color. The developed system non-invasively predicts lycopene content with high accuracy, offering an alternative to traditional methods.

Keywords:
HPLCRaspberry Pi 4artificial neuronal networkfuzzy logicimagelycopenetomato

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Area of Science:

  • Agrifood science
  • Biomedical engineering
  • Sensory analysis

Background:

  • Lycopene is a key quality and health indicator in tomatoes.
  • Traditional high-performance liquid chromatography (HPLC) is accurate but costly and time-consuming.
  • A non-destructive, cost-effective method for lycopene measurement is needed.

Purpose of the Study:

  • To develop a low-cost sensor for non-invasive lycopene quantification in tomatoes.
  • To correlate tomato epicarp color with lycopene content.
  • To evaluate predictive models for lycopene estimation.

Main Methods:

  • Utilized a Raspberry Pi 4 and Python for model development.
  • Employed digital color (RGB) input from tomato epicarp.
  • Evaluated regression models including neural networks, fuzzy logic, and linear regression.

Main Results:

  • Fuzzy nonlinear regression using RGB input achieved a high correlation (R² = 0.99).
  • The model demonstrated a low mean error of 1.9 × 10⁻⁵.
  • Successfully established a non-invasive method for lycopene determination.

Conclusions:

  • A low-cost, non-invasive sensor system can accurately predict tomato lycopene content.
  • This approach offers a viable alternative to destructive and expensive traditional methods.
  • Colorimetric analysis provides a practical tool for quality assessment in the agrifood industry.