Methodologies based on ASCA to elucidate the influence of a subprocess: Vinification as a case of study.

In food manufacturing and processing, food matrix complexity usually makes it difficult to detect unwanted subprocesses, which can impact the quality of the final product. In the case of wine alcoholic fermentation, the main process is the conversion of sugars into ethanol and carbon dioxide, but the presence of some unwanted microorganisms could lead to wine contamination by production of undesired minor compounds. In the study we present, an intentional contamination of the vinification process by the addition of acetic acid bacteria was studied using a portable Fourier transform infrared (FT‐IR) spectrometer. ANOVA simultaneous component analysis (ASCA) was used to unravel these minor variability sources. However, as the subprocess is two orders of magnitude lower in concentration than the main process, different methodologies were used to enhance the ASCA results, such as to select a specific spectral region related to acetic acid bacteria metabolism, to divide the process in time intervals related to the different phases, or to unfold the data matrix in different ways. In addition, spectral preprocessing was optimized to scale up small peaks related to the subprocess. Our results show that several methodologies to build ASCA models can be applied to emphasize and better characterize bacteria contamination subprocesses. Sugar to ethanol reaction in wine alcoholic fermentation hinders other undesired compound spectroscopic detection.Acetic acid bacteria contamination was studied by coupling a portable Fourier transform infrared spectrometer and ANOVA simultaneous component analysis.Selection of a specific spectral region, division of the process in time intervals, and different unfolding of the data matrix were applied to emphasize bacteria contamination. [ABSTRACT FROM AUTHOR]