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