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Related Experiment Video

Updated: Jun 26, 2026

Exploring the Application of Surface-enhanced Raman Scattering-based Biosensing of Individual sEVs in Disease Diagnosis and Therapeutics
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AI-Driven Patulin Detection in Apple Using Machine Learning Coupled With Surface-Enhanced Raman Spectroscopy.

Kwami Ashiagbor1, Heera Jayan1, Newton K Amaglo2

  • 1China Light Industry Key Laboratory of Food Intelligent Detection and Processing, School of Food Science and Engineering, Jiangsu University, Zhenjiang, China.

Journal of Food Science
|June 25, 2026
PubMed
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This summary is machine-generated.

Machine learning accurately predicts patulin (PAT) in apples using surface-enhanced Raman spectroscopy (SERS). Support Vector Machine (SVM) models combined with variable selection methods like UVE-SVM and GA-SVM offer the best accuracy for food safety.

Area of Science:

  • Food safety analysis
  • Spectroscopy
  • Chemometrics

Background:

  • Patulin (PAT) is a mycotoxin found in apples, posing health risks.
  • Conventional methods for PAT detection are time-consuming and complex.
  • Surface-enhanced Raman spectroscopy (SERS) offers a rapid detection alternative.

Purpose of the Study:

  • To evaluate machine learning algorithms for SERS-based PAT prediction in apples.
  • To optimize predictive accuracy and reduce computational load using variable selection.
  • To identify the most effective models for accurate PAT concentration determination.

Main Methods:

  • Implemented extreme learning machine (ELM), random forest (RF), and support vector machine (SVM) algorithms.
  • Utilized Competitive Adaptive Reweighted Sampling (CARS), Uninformative Variable Elimination (UVE), and Genetic Algorithm (GA) for variable selection.
Keywords:
SERSfood safetyfruitsmachine learningmodelingvegetables

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  • Evaluated model performance using calibration (Rc) and prediction (Rp) coefficients and Residual Predictive Deviation (RPD).

    • Main Results:

    • SVM-based methods demonstrated superior performance in PAT prediction.
    • UVE-SVM and GA-SVM achieved high predictive accuracy, with RPD values of 4.0487 and 4.3036, respectively.
    • Both UVE-SVM and GA-SVM models showed excellent calibration (Rc > 0.99) and prediction (Rp > 0.97) coefficients.

    Conclusions:

    • Variable selection is crucial for enhancing the performance of SERS-based PAT prediction models.
    • UVE-SVM and GA-SVM are recommended for highly accurate patulin concentration prediction in apples.
    • The study provides valuable insights for optimizing SERS spectral analysis in food safety applications.