Data-driven spectral stitching improves the performance of flow injection-mass spectrometry for food authentication: An example of pomegranate juice.

Authors

Hieu M Le, Ruixi Xie, Terry Koerner, Yaxi Hu

Published in

Food research international (Ottawa, Ont.). Volume 243. Issue Pt 1. Pages 120364. Nov 01, 2026. Epub Aug 11, 2026.

Abstract

Pomegranate juice is frequently adulterated with cheaper fruit species (e.g., apple, pear, and grape). A rapid flow-injection-mass spectrometry (FI-MS) method coupled with machine learning (ML) was developed for pomegranate juice authentication. A data-driven spectral-stitching strategy was designed to improve authentication accuracy. After comparing different ML algorithms and models constructed using different datasets, an optimized hierarchical classification workflow was established. Specifically, a 7-class random forest (RF) model was used to differentiate 4 species of pure juices and 3 types of adulterated juice, followed by a semi-quantification model to recognize adulteration level. This workflow detected apple and pear at low concentrations (1-5%), and ∼ 10% for grape due to the chemical similarity with pomegranate. The 100% accuracy for recognizing commercial pure pomegranate juice further validated robustness of the workflow. This optimized workflow combining spectral-stitching FI-MS with RF modeling provides a rapid, reproducible framework for juice authentication that is extendable to other food commodities.

PMID:
42692744
Bibliographic data and abstract were imported from PubMed on 04 Sep 2026.

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