Literature DB >> 31253256

A comparison of electronic nose and gas chromatography-mass spectrometry on discrimination and prediction of ochratoxin A content in Aspergillus carbonarius cultured grape-based medium.

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.
Copyright © 2019 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Aspergillus carbonarius; Electronic nose; GC–MS; Ochratoxin A; Partial least squares regression; Partial least squares-discriminant analysis

Mesh:

Substances:

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


  8 in total

1.  Fluonanobody-based nanosensor via fluorescence resonance energy transfer for ultrasensitive detection of ochratoxin A.

Authors:  Benchao Su; Zhong Zhang; Zhichang Sun; Zongwen Tang; Xiaoxia Xie; Qi Chen; Hongmei Cao; Xi Yu; Yang Xu; Xing Liu; Bruce D Hammock
Journal:  J Hazard Mater       Date:  2021-08-06       Impact factor: 10.588

2.  Application of MOS Gas Sensors Coupled with Chemometrics Methods to Predict the Amount of Sugar and Carbohydrates in Potatoes.

Authors:  Ali Khorramifar; Mansour Rasekh; Hamed Karami; James A Covington; Sayed M Derakhshani; Jose Ramos; Marek Gancarz
Journal:  Molecules       Date:  2022-05-30       Impact factor: 4.927

Review 3.  Progress of Research on the Application of Nanoelectronic Smelling in the Field of Food.

Authors:  Junjiang Sha; Chong Xu; Ke Xu
Journal:  Micromachines (Basel)       Date:  2022-05-18       Impact factor: 3.523

4.  Ultrasensitive and rapid detection of ochratoxin A in agro-products by a nanobody-mediated FRET-based immunosensor.

Authors:  Zongwen Tang; Xing Liu; Benchao Su; Qi Chen; Hongmei Cao; Yonghuan Yun; Yang Xu; Bruce D Hammock
Journal:  J Hazard Mater       Date:  2019-11-12       Impact factor: 10.588

5.  Antitumor, Antiviral, and Anti-Inflammatory Efficacy of Essential Oils from Atractylodes macrocephala Koidz. Produced with Different Processing Methods.

Authors:  Sihao Gu; Ling Li; Hai Huang; Bing Wang; Tong Zhang
Journal:  Molecules       Date:  2019-08-15       Impact factor: 4.411

6.  Analysis of volatile emissions from grape berries infected with Aspergillus carbonarius using hyphenated and portable mass spectrometry.

Authors:  Konstantinos Giannoukos; Stamatios Giannoukos; Christina Lagogianni; Dimitrios I Tsitsigiannis; Stephen Taylor
Journal:  Sci Rep       Date:  2020-12-03       Impact factor: 4.379

7.  Dramatically Enhancing the Sensitivity of Immunoassay for Ochratoxin A Detection by Cascade-Amplifying Enzyme Loading.

Authors:  Zhuolin Song; Lin Feng; Yuankui Leng; Mingzhu Huang; Hao Fang; Weipeng Tong; Xuelan Chen; Yonghua Xiong
Journal:  Toxins (Basel)       Date:  2021-11-05       Impact factor: 4.546

8.  Enzyme cascade-amplified immunoassay based on the nanobody-alkaline phosphatase fusion and MnO2 nanosheets for the detection of ochratoxin A in coffee.

Authors:  Zeling Zhang; Benchao Su; Huan Xu; Zhenyun He; Yuling Zhou; Qi Chen; Zhichang Sun; Hongmei Cao; Xing Liu
Journal:  RSC Adv       Date:  2021-06-21       Impact factor: 4.036

  8 in total

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