| Literature DB >> 31253256 |
Xiaoxu Zhang1, Menghua Li2, Zhan Cheng2, Liyan Ma3, Longlian Zhao4, Jingming Li5.
Abstract
This study investigated discrimination and prediction of ochratoxin A (OTA) in three Aspergillus carbonarius strains cultured grape-based medium using E-nose technology and GC-MS analysis. Results showed that these strains cultured medium samples were divided into four groups regarding their log 10 OTA value using an equispaced normal distribution analysis. Partial least squares-discriminant analysis (PLS-DA) revealed that GC-MS PLS-DA model only separated the low OTA level medium samples from the rest OTA level samples, whereas all the OTA level samples were segregated from each other using E-nose PLS-DA model. Partial least squares regression (PLSR) analysis indicated that an excellent prediction performance was established on the accumulation of OTA in these medium samples using E-nose PLSR, whereas GC-MS PLSR model showed a screening performance on the OTA formation. These indicated that E-nose analysis could be a reliable method on discriminating and predicting OTA in A. carbonarius strains under grape-based medium.Entities:
Keywords: Aspergillus carbonarius; Electronic nose; GC–MS; Ochratoxin A; Partial least squares regression; Partial least squares-discriminant analysis
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Year: 2019 PMID: 31253256 DOI: 10.1016/j.foodchem.2019.05.124
Source DB: PubMed Journal: Food Chem ISSN: 0308-8146 Impact factor: 7.514