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电子鼻技术用于检测软质小麦中的麦角生物碱,并通过固相微萃取/气相色谱-高分辨率轨道阱质谱联用化学计量学方法鉴定相关挥发性化合物。

Electronic nose technology for the detection of ergot alkaloid in soft wheat and identification of the relevant volatile compounds by solid phase microextraction/gas chromatography-high resolution Orbitrap-mass spectrometry coupled to chemometrics.

作者信息

Piergiovanni Maurizio, Giliberti Chiara, Maffezzoni Cristian, Errico Davide, Blandino Massimo, Dall'Asta Chiara, Mattarozzi Monica, Bianchi Federica, Giannetto Marco, Careri Maria

机构信息

Department of Chemistry, Life Sciences and Environmental Sustainability, University of Parma, Parco Area delle Scienze 17/A, 43124 Parma, Italy.

Department of Agricultural, Forest and Food Sciences, University of Turin, Largo Paolo Braccini 2, Grugliasco 10095, Italy.

出版信息

Food Chem. 2025 Aug 30;484:144455. doi: 10.1016/j.foodchem.2025.144455. Epub 2025 Apr 22.

DOI:10.1016/j.foodchem.2025.144455
PMID:40288212
Abstract

Ergot alkaloids (EA) are mycotoxins produced by Claviceps purpurea which commonly infects various cereal species, compromising food safety. This study evaluates the potential of the electronic nose to reliably predict EA contamination in wheat, demonstrating as a proof-of-concept the ability of this technology combined with supervised techniques to distinguish samples contaminated at levels of interest from compliant samples. In particular, the average value of samples correctly classified using PLS-DA was 95.5 %. Furthermore, a volatilomics approach based on HS-SPME/GC-Orbitrap HRMS and chemometrics was successfully applied for the first time to characterize the volatile compound pattern of wheat samples based on the level of EA contamination paying attention to the secondary volatile metabolites. Overall, a high confidence in compound identification was achieved with sub-1 ppm mass accuracy. Unsupervised PCA was used for discrimination purposes, revealing 19 differential compounds (markers), some of which are released during the growth of Claviceps Purpurea fungi.

摘要

麦角生物碱(EA)是由紫麦角菌产生的霉菌毒素,紫麦角菌通常感染各种谷物品种,危害食品安全。本研究评估了电子鼻可靠预测小麦中EA污染的潜力,作为概念验证证明了该技术结合监督技术能够将感兴趣水平污染的样品与合格样品区分开来。特别是,使用偏最小二乘判别分析(PLS-DA)正确分类的样品的平均值为95.5%。此外,基于顶空固相微萃取/气相色谱-轨道阱高分辨质谱(HS-SPME/GC-Orbitrap HRMS)和化学计量学的挥发物组学方法首次成功应用于根据EA污染水平表征小麦样品的挥发性化合物模式,同时关注次生挥发性代谢物。总体而言,在质量准确度低于1 ppm的情况下,化合物鉴定具有很高的可信度。无监督主成分分析(PCA)用于判别目的,揭示了19种差异化合物(标志物),其中一些是在紫麦角菌生长过程中释放的。

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