电子鼻
气相色谱-质谱法
化学
质谱法
认证(法律)
食品科学
色谱法
固相微萃取
计算机科学
人工智能
计算机安全
作者
Salvatore Cervellieri,Vincenzo Lippolis,Erminia Mancini,Michelangelo Pascale,Antonio Logrieco,Annalisa De Girolamo
出处
期刊:Food Chemistry
[Elsevier]
日期:2022-02-25
卷期号:383: 132548-132548
被引量:25
标识
DOI:10.1016/j.foodchem.2022.132548
摘要
Headspace solid-phase microextraction (HS-SPME) coupled with mass spectrometry-based electronic nose (MS-eNose), in combination with multivariate statistical analysis was used as untargeted method for the rapid authentication of 100% Italian durum wheat pasta. Among the tested classification models, i.e. PCA-LDA, PLS-DA and SVMc, SVMc provided the highest accuracy results in both calibration (90%) and validation (92%) processes. Potential markers discriminating pasta samples were identified by HS-SPME/GC-MS analysis. Specifically, the content of a pattern of 8 out of 59 volatile organic compounds (VOCs) was significantly different between samples of 100% Italian durum wheat pasta and pasta produced with durum wheat of different origins, most of which were related to different lipidic oxidation in the two classes of pasta. The proposed MS-eNose method is a rapid and reliable tool to be used for authenticating Italian pasta useful to promote its typicity and preserving consumers from fraudulent practices.
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