Early Detection of Fungal Growth in Bakery Products by Use of an Electronic Nose Based on Mass Spectrometry

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• 作者：Maria Vinaixa ; Sonia Marí ; n ; Jesú ; s Brezmes ; Eduard Llobet ; Xavier Vilanova ; Xavier Correig ; Antonio Ramos ; and Vicent Sanchis
• 刊名：Journal of Agricultural and Food Chemistry
• 出版年：2004
• 出版时间：October 6, 2004
• 年：2004
• 卷：52
• 期：20
• 页码：6068 - 6074
• 全文大小：118K
• 年卷期：v.52,no.20(October 6, 2004)
• ISSN：1520-5118

文摘

This paper presents the design, optimization, and evaluation of a mass spectrometry-based electronicnose (MS e-nose) for early detection of unwanted fungal growth in bakery products. Seven fungalspecies (Aspergillus flavus, Aspergillus niger, Eurotium amstelodami, Eurotium herbariorum, Eurotiumrubrum, Eurotium repens, and Penicillium corylophillum) were isolated from bakery products andused for the study. Two sampling headspace techniques were tested: static headspace (SH) andsolid-phase microextraction (SPME). Cross-validated models based on principal component analysis(PCA), coupled to discriminant function analysis (DFA) and fuzzy ARTMAP, were used as datatreatment. When attempting to discriminate between inoculated and blank control vials or betweengenera or species of in vitro growing cultures, sampling based on SPME showed better results thanthose based on static headspace. The SPME-MS-based e-nose was able to predict fungal growthwith 88% success after 24 h of inoculation and 98% success after 48 h when changes were monitoredin the headspace of fungal cultures growing on bakery product analogues. Prediction of the rightfungal genus reached 78% and 88% after 24 and 96 h, respectively.Keywords: Electronic nose; mass spectrometry; fungal growth; bakery products; fuzzy ARTMAP; ANN;LDA; PCA