AI-Driven Patulin Detection in Apple Using Machine Learning Coupled With Surface-Enhanced Raman Spectroscopy.

Authors

Kwami Ashiagbor, Heera Jayan, Newton K Amaglo, Xiaobo Zou, Zhiming Guo

Published in

Journal of food science. Volume 91. Issue 6. Pages e71250.

Abstract

This study presents a comprehensive evaluation of machine learning approaches for surface-enhanced Raman spectroscopy (SERS)-based prediction of patulin (PAT) concentration in apples, a critical food safety concern due to its toxic effects on health and limitations of conventional laboratory-based detection methods. Three base algorithms, extreme learning machine (ELM), random forest (RF), and support vector machine (SVM), were implemented in combination with three variable selection methods: Competitive Adaptive Reweighted Sampling (CARS), Uninformative Variable Elimination (UVE), and Genetic Algorithm (GA). The investigation focused on optimizing predictive accuracy while reducing computational complexity through effective variable selection. Results demonstrated that SVM-based methods achieved superior performance. UVE-SVM and GA-SVM exhibited exceptional predictive capabilities with Residual Predictive Deviation (RPD) values of 4.0487 and 4.3036, respectively, and outstanding calibration (R_c > 0.99) and prediction (R_p > 0.97) coefficients. This study provided valuable insights for optimizing SERS spectral analysis in food safety applications, recommending UVE-SVM or GA-SVM implementations for the highest accuracy in PAT concentration prediction while emphasizing the crucial role of variable selection in enhancing model performance.

PMID:
42348177
Bibliographic data and abstract were imported from PubMed on 25 Jun 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.