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Estimating Acrylamide and 5-Hydroxymethylfurfural Levels in Crackers Using Computer Vision: Effects on Consumer

Franco Pedreschi1, Darwin Castillo2, Andrea Bunger2

  • 1Departamento de Ingeniería Química y Bioprocesos, Facultad de Ingeniería, Pontificia Universidad Católica de Chile, P.O. Box 306, Santiago 6904411, Chile.

Foods (Basel, Switzerland)
|June 12, 2026
PubMed
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This summary is machine-generated.

Computer vision models rapidly predict acrylamide (AA) and 5-hydroxymethylfurfural (HMF) in crackers, reducing reliance on complex traditional methods. This technology offers efficient quality control for food safety and consumer preference.

Area of Science:

  • Food Science
  • Analytical Chemistry
  • Computer Vision

Background:

  • Baking crackers generates neo-formed contaminants (NFCs) like acrylamide (AA) and 5-hydroxymethylfurfural (HMF) via non-enzymatic browning.
  • Conventional methods for quantifying NFCs are time-consuming, labor-intensive, and require specialized expertise.

Purpose of the Study:

  • To develop and validate computer vision (CV) models for rapid prediction of AA and HMF in crackers.
  • To assess the efficiency of CV in estimating NFCs compared to traditional analytical techniques.

Main Methods:

  • Digital image analysis using CV models was employed to analyze cracker surface characteristics.
  • Five baking temperatures (160-200 °C) and times (15-35 min) were tested.
  • CV predictions were cross-validated against conventional analytical measurements for AA and HMF.
Keywords:
GC-MSHPLC-DADcross-validationimage analysismodelingsensory testing

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Main Results:

  • CV models achieved an average error of 3.10% for AA and 3.28% for HMF, demonstrating high accuracy.
  • The study successfully correlated cracker baking conditions with NFC levels.
  • Consumer preference studies indicated a preference for samples baked at 180 °C for 25 min, which had the lowest AA and HMF.

Conclusions:

  • Computer vision is an effective and rapid tool for estimating AA and HMF in crackers.
  • CV can aid in optimizing baking processes to minimize NFC formation while meeting consumer preferences.
  • This approach offers a promising alternative for quality control in the food industry.