Premium
Aroma compounds evolution in fruit spirits under different storage conditions analyzed with multiway anova and artificial neural networks
Author(s) -
MatiasGuiu Pau,
RodríguezBencomo Juan José,
PérezCorrea José R.,
López Francisco
Publication year - 2020
Publication title -
journal of food processing and preservation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.511
H-Index - 48
eISSN - 1745-4549
pISSN - 0145-8892
DOI - 10.1111/jfpp.14410
Subject(s) - bottling line , organoleptic , aroma , food science , acetaldehyde , chemistry , artificial neural network , biochemical engineering , mathematics , computer science , artificial intelligence , ethanol , organic chemistry , engineering , wine
For producers, distributors, and restaurateurs, it is essential to understand the variation of the volatile composition of bottled spirits under different storage conditions. Given the scarce information found in this regard, the present study investigates the effect of pH, temperature, light exposure, and time of storage on 18 major volatile compounds of a fruit spirit. To carry out this longitudinal study, a central composite design was applied repeatedly over a year. Multi‐way ANOVA and artificial neural networks were used to analyze and model the process. The results show that high temperatures sharply reduce most spirit compounds, especially acetaldehyde, ethyl esters, and linalool. In addition, under standard conditions, most compounds undergo a concentration decrease during the first 20 days of storage and then their composition becomes stable. Most of the other studied conditions showed noticeable effects, although without significant compositional differences. Practical applications The production of alcoholic beverages usually includes a maturation time after distillation and before bottling in order to stabilize the organoleptic characteristics of the product. However, the storage conditions after bottling can affect these organoleptic characteristics, and therefore, to the shelf life of the product. The results presented in this study, evaluating different storage conditions, can be useful not only to keep the organoleptic characteristics, but also to modify these in a controlled way. In addition, the methodology used for the data analysis, based on multi‐way ANOVA and artificial neural networks analysis, can be useful in the studies of other types of food products.