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 thequality of thefinal product. In the case of wine alcoholic fermentation, themain process is the conversion of sugars into ethanol and carbon dioxide, but the presence of some unwanted microorganismscould lead to wine contamination by production of undesired minor compounds. In thestudywe present, an intentional contamination of the vinification processby the addition ofacetic acid bacteria was studied using a portable Fourier Transform Infrared (FT-IR) spectrometer. ANOVA Simultaneous Component Analysis (ASCA) was usedto unravel these minor variability sources. However, as the subprocess is twoorders of magnitude lowerin concentration than the main process, different methodologies were used to enhance theASCAresults, suchasto selecta specific spectral regionrelated to acetic acid bacteria metabolism, to dividethe process in time intervals related to the different phases, or to unfold the data matrix in different ways. In addition, spectral pre-processing 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.