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Detection of Maize Mold Based on a Nanocomposite Colorimetric Sensor Array under Different Substrates.

Hao Lin1, Zeyu Chen1, Selorm Yao-Say Solomon Adade2

  • 1School of Food and Biological Engineering, Jiangsu University, No. 301 Xuefu Road, Jiangsu 212013, P. R. China.

Journal of Agricultural and Food Chemistry
|April 2, 2024
PubMed
Summary

A new nanocomposite colorimetric sensor array (CSA) accurately distinguishes fresh from moldy maize. This method identifies key volatile organic compounds (VOCs) for rapid grain quality control.

Keywords:
colorimetric sensor arraymaizemoldsubstrate screeningvolatile organic compound

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Area of Science:

  • Agricultural Science
  • Analytical Chemistry
  • Materials Science

Background:

  • Mold contamination in maize poses significant food safety risks.
  • Accurate and rapid detection of moldy maize is crucial for quality control.

Purpose of the Study:

  • To develop a novel nanocomposite colorimetric sensor array (CSA) for distinguishing fresh from moldy maize.
  • To identify key volatile organic compounds (VOCs) indicative of maize mold contamination.
  • To optimize CSA fabrication using various nanoparticles and substrates for enhanced detection.

Main Methods:

  • Headspace solid-phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC/MS) for VOC analysis.
  • Principal component analysis (PCA) and orthogonal partial least-squares discriminant analysis (OPLS-DA) for VOC identification.
  • Fabrication of nanocomposite CSAs using modified colorimetric dyes and nanoparticles (polystyrene acrylic, porous silica nanospheres, ZIF-8).
  • Evaluation of different substrates (filter paper, PVDF film, TLC silica gel) and machine learning models (LDA, KNN) for detection.

Main Results:

  • 2-methylbutyric acid and undecane identified as key VOCs in moldy maize.
  • Four types of nanocomposite colorimetric sensitive dyes were synthesized and confirmed.
  • All moldy maize samples were correctly identified using the developed CSA.
  • Linear discriminant analysis (LDA) and K-nearest neighbor (KNN) models achieved 100% accuracy.

Conclusions:

  • The developed nanocomposite CSA offers a highly accurate method for distinguishing fresh and moldy maize.
  • This sensor array shows significant potential for on-site, rapid grain quality assessment.
  • The integration of nanotechnology and colorimetric sensing provides a promising platform for food safety applications.